Adding ReflectExecutors method to Workflow.

2026-06-16 21:04:09 +08:00 · 2026-01-22 12:07:03 -08:00
2156 changed files with 80416 additions and 142117 deletions
@@ -1,34 +1,16 @@
 {
  "name": "C# (.NET)",
-  //"image": "mcr.microsoft.com/devcontainers/dotnet",
-  // Workaround for https://github.com/devcontainers/images/issues/1752
-  "build": {
-    "dockerfile": "dotnet.Dockerfile"
-  },
+  "image": "mcr.microsoft.com/devcontainers/dotnet:10.0",
  "features": {
-    "ghcr.io/devcontainers/features/azure-cli:1.2.9": {},
-    "ghcr.io/devcontainers/features/github-cli:1": {
-      "version": "2"
-    },
-    "ghcr.io/devcontainers/features/powershell:1": {
-      "version": "latest"
-    },
-    "ghcr.io/azure/azure-dev/azd:0": {
-      "version": "latest"
-    },
-    "ghcr.io/devcontainers/features/dotnet:2": {
-      "version": "none",
-      "dotnetRuntimeVersions": "10.0",
-      "aspNetCoreRuntimeVersions": "10.0"
-    },
-    "ghcr.io/devcontainers/features/copilot-cli:1": {}
+    "ghcr.io/devcontainers/features/dotnet:2.4.0": {},
+    "ghcr.io/devcontainers/features/powershell:1.5.1": {},
+    "ghcr.io/devcontainers/features/azure-cli:1.2.8": {},
+    "ghcr.io/devcontainers/features/docker-in-docker:2.12.4": {}
  },
  "workspaceFolder": "/workspaces/agent-framework/dotnet/",
  "customizations": {
    "vscode": {
      "extensions": [
-        "GitHub.copilot",
-        "GitHub.vscode-github-actions",
        "ms-dotnettools.csdevkit",
        "vscode-icons-team.vscode-icons",
        "ms-windows-ai-studio.windows-ai-studio"
@@ -1,5 +0,0 @@
-FROM mcr.microsoft.com/devcontainers/universal:latest
-
-# Remove Yarn repository with expired GPG key to prevent apt-get update failures
-# Tracking issue: https://github.com/devcontainers/images/issues/1752
-RUN rm -f /etc/apt/sources.list.d/yarn.list
@@ -2,6 +2,3 @@
 # https://docs.github.com/repositories/managing-your-repositorys-settings-and-features/customizing-your-repository/about-code-owners

 python/packages/azurefunctions/ @microsoft/agentframework-durabletask-developers
-python/packages/durabletask/ @microsoft/agentframework-durabletask-developers
-python/samples/getting_started/azure_functions/ @microsoft/agentframework-durabletask-developers
-python/samples/getting_started/durabletask/ @microsoft/agentframework-durabletask-developers
@@ -1,19 +1,67 @@
 # GitHub Copilot Instructions

-Microsoft Agent Framework - a multi-language framework for building, orchestrating, and deploying AI agents.
+This repository contains both Python and C# code.
+All python code resides under the `python/` directory.
+All C# code resides under the `dotnet/` directory.

-## Repository Structure
+The purpose of the code is to provide a framework for building AI agents.

- `python/` - Python implementation → see [python/AGENTS.md](../python/AGENTS.md)
- `dotnet/` - C#/.NET implementation → see [dotnet/AGENTS.md](../dotnet/AGENTS.md)
- `docs/` - Design documents and architectural decision records
+When contributing to this repository, please follow these guidelines:

-## Architectural Decision Records (ADRs)
+## C# Code Guidelines

-ADRs in `docs/decisions/` capture significant design decisions and their rationale. They document considered alternatives, trade-offs, and the reasoning behind choices.
+Here are some general guidelines that apply to all code.

-**Templates:**
- `adr-template.md` - Full template with detailed sections
- `adr-short-template.md` - Abbreviated template for simpler decisions
+- The top of all *.cs files should have a copyright notice: `// Copyright (c) Microsoft. All rights reserved.`
+- All public methods and classes should have XML documentation comments.

-When proposing architectural changes, create an ADR to capture options considered and the decision rationale. See [docs/decisions/README.md](../docs/decisions/README.md) for the full process.
+### C# Sample Code Guidelines
+
+Sample code is located in the `dotnet/samples` directory.
+
+When adding a new sample, follow these steps:
+
+- The sample should be a standalone .net project in one of the subdirectories of the samples directory.
+- The directory name should be the same as the project name.
+- The directory should contain a README.md file that explains what the sample does and how to run it.
+- The README.md file should follow the same format as other samples.
+- The csproj file should match the directory name.
+- The csproj file should be configured in the same way as other samples.
+- The project should preferably contain a single Program.cs file that contains all the sample code.
+- The sample should be added to the solution file in the samples directory.
+- The sample should be tested to ensure it works as expected.
+- A reference to the new samples should be added to the README.md file in the parent directory of the new sample.
+
+The sample code should follow these guidelines:
+
+- Configuration settings should be read from environment variables, e.g. `var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");`.
+- Environment variables should use upper snake_case naming convention.
+- Secrets should not be hardcoded in the code or committed to the repository.
+- The code should be well-documented with comments explaining the purpose of each step.
+- The code should be simple and to the point, avoiding unnecessary complexity.
+- Prefer inline literals over constants for values that are not reused. For example, use `new ChatClientAgent(chatClient, instructions: "You are a helpful assistant.")` instead of defining a constant for "instructions".
+- Ensure that all private classes are sealed
+- Use the Async suffix on the name of all async methods that return a Task or ValueTask.
+- Prefer defining variables using types rather than var, to help users understand the types involved.
+- Follow the patterns in the samples in the same directories where new samples are being added.
+- The structure of the sample should be as follows:
+  - The top of the Program.cs should have a copyright notice: `// Copyright (c) Microsoft. All rights reserved.`
+  - Then add a comment describing what the sample is demonstrating.
+  - Then add the necessary using statements.
+  - Then add the main code logic.
+  - Finally, add any helper methods or classes at the bottom of the file.
+
+### C# Unit Test Guidelines
+
+Unit tests are located in the `dotnet/tests` directory in projects with a `.UnitTests.csproj` suffix.
+
+Unit tests should follow these guidelines:
+
+- Use `this.` for accessing class members
+- Add Arrange, Act and Assert comments for each test
+- Ensure that all private classes, that are not subclassed, are sealed
+- Use the Async suffix on the name of all async methods
+- Use the Moq library for mocking objects where possible
+- Validate that each test actually tests the target behavior, e.g. we should not have tests that creates a mock, calls the mock and then verifies that the mock was called, without the target code being involved. We also shouldn't have tests that test language features, e.g. something that the compiler would catch anyway.
+- Avoid adding excessive comments to tests. Instead favour clear easy to understand code.
+- Follow the patterns in the unit tests in the same project or classes to which new tests are being added
@@ -7,13 +7,6 @@ name: dotnet-build-and-test

 on:
  workflow_dispatch:
-  workflow_call:
-    inputs:
-      checkout-ref:
-        description: "Git ref to checkout (e.g., a commit SHA from a PR)"
-        required: false
-        type: string
-        default: ""
  pull_request:
    branches: ["main", "feature*"]
  merge_group:
@@ -46,8 +39,6 @@ jobs:
      cosmosDbChanges: ${{ steps.filter.outputs.cosmosdb }}
    steps:
      - uses: actions/checkout@v6
-        with:
-          ref: ${{ inputs.checkout-ref }}
      - uses: dorny/paths-filter@v3
        id: filter
        with:
@@ -85,7 +76,6 @@ jobs:
    steps:
      - uses: actions/checkout@v6
        with:
-            ref: ${{ inputs.checkout-ref }}
            persist-credentials: false
            sparse-checkout: |
              .
@@ -93,7 +83,7 @@ jobs:
              dotnet
              python
              workflow-samples
-
+              
      # Start Cosmos DB Emulator for all integration tests and only for unit tests when CosmosDB changes happened)
      - name: Start Azure Cosmos DB Emulator
        if: ${{ runner.os == 'Windows' && (needs.paths-filter.outputs.cosmosDbChanges == 'true' || (github.event_name != 'pull_request' && matrix.integration-tests)) }}
@@ -149,7 +139,7 @@ jobs:
          popd
          popd
          rm -rf "$TEMP_DIR"
-
+          
      - name: Run Unit Tests
        shell: bash
        run: |
@@ -157,7 +147,7 @@ jobs:
          for project in $UT_PROJECTS; do
            # Query the project's target frameworks using MSBuild with the current configuration
            target_frameworks=$(dotnet msbuild $project -getProperty:TargetFrameworks -p:Configuration=${{ matrix.configuration }} -nologo 2>/dev/null | tr -d '\r')
-
+            
            # Check if the project supports the target framework
            if [[ "$target_frameworks" == *"${{ matrix.targetFramework }}"* ]]; then
              if [[ "${{ matrix.targetFramework }}" == "${{ env.COVERAGE_FRAMEWORK }}" ]]; then
@@ -175,8 +165,8 @@ jobs:
          COSMOSDB_KEY: C2y6yDjf5/R+ob0N8A7Cgv30VRDJIWEHLM+4QDU5DE2nQ9nDuVTqobD4b8mGGyPMbIZnqyMsEcaGQy67XIw/Jw==

      - name: Log event name and matrix integration-tests
-        shell: bash
-        run: echo "github.event_name:${{ github.event_name }} matrix.integration-tests:${{ matrix.integration-tests }} github.event.action:${{ github.event.action }} github.event.pull_request.merged:${{ github.event.pull_request.merged }}"
+        shell: bash 
+        run: echo "github.event_name:${{ github.event_name }} matrix.integration-tests:${{ matrix.integration-tests }} github.event.action:${{ github.event.action }} github.event.pull_request.merged:${{ github.event.pull_request.merged }}" 

      - name: Azure CLI Login
        if: github.event_name != 'pull_request' && matrix.integration-tests
@@ -202,10 +192,10 @@ jobs:
          for project in $INTEGRATION_TEST_PROJECTS; do
            # Query the project's target frameworks using MSBuild with the current configuration
            target_frameworks=$(dotnet msbuild $project -getProperty:TargetFrameworks -p:Configuration=${{ matrix.configuration }} -nologo 2>/dev/null | tr -d '\r')
-
+            
            # Check if the project supports the target framework
            if [[ "$target_frameworks" == *"${{ matrix.targetFramework }}"* ]]; then
-              dotnet test -f ${{ matrix.targetFramework }} -c ${{ matrix.configuration }} $project --no-build -v Normal --logger trx --filter "Category!=IntegrationDisabled"
+              dotnet test -f ${{ matrix.targetFramework }} -c ${{ matrix.configuration }} $project --no-build -v Normal --logger trx
            else
              echo "Skipping $project - does not support target framework ${{ matrix.targetFramework }} (supports: $target_frameworks)"
            fi
@@ -1,106 +0,0 @@
-#
-# This workflow allows manually running integration tests against an open PR or a branch.
-# Go to Actions → "Integration Tests (Manual)" → Run workflow → enter a PR number or branch name.
-#
-# It reuses the existing dotnet-build-and-test and python-merge-tests workflows,
-# passing a ref so they check out and test the correct code.
-#
-
-name: Integration Tests (Manual)
-
-on:
-  workflow_dispatch:
-    inputs:
-      pr-number:
-        description: "PR number to run integration tests against (leave empty if using branch)"
-        required: false
-        type: string
-        default: ""
-      branch:
-        description: "Branch name to run integration tests against (leave empty if using PR number)"
-        required: false
-        type: string
-        default: ""
-
-permissions:
-  contents: read
-  pull-requests: read
-  id-token: write
-
-concurrency:
-  group: integration-tests-manual-${{ github.event.inputs.pr-number || github.event.inputs.branch }}
-  cancel-in-progress: true
-
-jobs:
-  resolve-ref:
-    name: Resolve ref
-    runs-on: ubuntu-latest
-    outputs:
-      checkout-ref: ${{ steps.resolve.outputs.checkout-ref }}
-    steps:
-      - name: Resolve checkout ref
-        id: resolve
-        env:
-          GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-          PR_NUMBER: ${{ github.event.inputs.pr-number }}
-          BRANCH: ${{ github.event.inputs.branch }}
-          REPO: ${{ github.repository }}
-          REPO_OWNER: ${{ github.repository_owner }}
-        run: |
-          if [ -n "$PR_NUMBER" ] && [ -n "$BRANCH" ]; then
-            echo "::error::Please provide either a PR number or a branch name, not both."
-            exit 1
-          fi
-
-          if [ -z "$PR_NUMBER" ] && [ -z "$BRANCH" ]; then
-            echo "::error::Please provide either a PR number or a branch name."
-            exit 1
-          fi
-
-          if [ -n "$PR_NUMBER" ]; then
-            if ! echo "$PR_NUMBER" | grep -Eq '^[0-9]+$'; then
-              echo "::error::Invalid PR number. Only numeric values are allowed."
-              exit 1
-            fi
-
-            PR_DATA=$(gh pr view "$PR_NUMBER" --repo "$REPO" --json state,headRepository,headRepositoryOwner)
-            PR_STATE=$(echo "$PR_DATA" | jq -r '.state')
-            HEAD_OWNER=$(echo "$PR_DATA" | jq -r '.headRepositoryOwner.login')
-
-            if [ "$PR_STATE" != "OPEN" ]; then
-              echo "::error::PR #$PR_NUMBER is not open (state: $PR_STATE)"
-              exit 1
-            fi
-
-            if [ "$HEAD_OWNER" != "$REPO_OWNER" ]; then
-              echo "::error::PR #$PR_NUMBER is from a fork ($HEAD_OWNER). Running integration tests against fork PRs is not allowed for security reasons."
-              exit 1
-            fi
-
-            echo "checkout-ref=refs/pull/$PR_NUMBER/head" >> "$GITHUB_OUTPUT"
-            echo "Running integration tests for PR #$PR_NUMBER"
-          else
-            if ! echo "$BRANCH" | grep -Eq '^[a-zA-Z0-9_./-]+$'; then
-              echo "::error::Invalid branch name. Only alphanumeric characters, hyphens, underscores, dots, and slashes are allowed."
-              exit 1
-            fi
-
-            echo "checkout-ref=$BRANCH" >> "$GITHUB_OUTPUT"
-            echo "Running integration tests for branch $BRANCH"
-          fi
-
-  dotnet-integration-tests:
-    name: .NET Integration Tests
-    needs: resolve-ref
-    uses: ./.github/workflows/dotnet-build-and-test.yml
-    with:
-      checkout-ref: ${{ needs.resolve-ref.outputs.checkout-ref }}
-    secrets: inherit
-
-  python-integration-tests:
-    name: Python Integration Tests
-    needs: resolve-ref
-    uses: ./.github/workflows/python-merge-tests.yml
-    with:
-      checkout-ref: ${{ needs.resolve-ref.outputs.checkout-ref }}
-    secrets: inherit
@@ -1,383 +0,0 @@
-#!/usr/bin/env python3
-# Copyright (c) Microsoft. All rights reserved.
-"""Check Python test coverage against threshold for enforced targets.
-
-This script parses a Cobertura XML coverage report and enforces a minimum
-coverage threshold on specific targets. Targets can be package names
-(e.g., "packages.core.agent_framework") or individual Python file paths
-(e.g., "packages/core/agent_framework/observability.py").
-
-Non-enforced targets are reported for visibility but don't block the build.
-
-Usage:
-    python python-check-coverage.py <coverage-xml-path> <threshold>
-
-Example:
-    python python-check-coverage.py python-coverage.xml 85
-"""
-
-import sys
-import xml.etree.ElementTree as ET
-from dataclasses import dataclass
-
-# =============================================================================
-# ENFORCED TARGETS CONFIGURATION
-# =============================================================================
-# Add or remove entries from this set to control which targets must meet
-# the coverage threshold. Only these targets will fail the build if below
-# threshold. Other targets are reported for visibility only.
-#
-# Target values can be:
-# - Package paths as they appear in the coverage report
-#   (e.g., "packages.azure-ai.agent_framework_azure_ai")
-# - Python source file paths as they appear in the coverage report
-#   (e.g., "packages/core/agent_framework/observability.py")
-# =============================================================================
-ENFORCED_TARGETS: set[str] = {
-    # Packages
-    "packages.azure-ai.agent_framework_azure_ai",
-    "packages.core.agent_framework",
-    "packages.core.agent_framework._workflows",
-    "packages.purview.agent_framework_purview",
-    "packages.anthropic.agent_framework_anthropic",
-    "packages.azure-ai-search.agent_framework_azure_ai_search",
-    "packages.core.agent_framework.azure",
-    "packages.core.agent_framework.openai",
-    # Individual files (if you want to enforce specific files instead of whole packages)
-    "packages/core/agent_framework/observability.py",
-    # Add more targets here as coverage improves
-}
-
-
-@dataclass
-class PackageCoverage:
-    """Coverage data for a single package."""
-
-    name: str
-    line_rate: float
-    branch_rate: float
-    lines_valid: int
-    lines_covered: int
-    branches_valid: int
-    branches_covered: int
-
-    @property
-    def line_coverage_percent(self) -> float:
-        """Return line coverage as a percentage."""
-        return self.line_rate * 100
-
-    @property
-    def branch_coverage_percent(self) -> float:
-        """Return branch coverage as a percentage."""
-        return self.branch_rate * 100
-
-
-def normalize_coverage_path(path: str) -> str:
-    """Normalize coverage paths for reliable matching."""
-    return path.replace("\\", "/").lstrip("./")
-
-
-def parse_coverage_xml(
-    xml_path: str,
-) -> tuple[dict[str, PackageCoverage], dict[str, PackageCoverage], float, float]:
-    """Parse Cobertura XML and extract per-package coverage data.
-
-    Args:
-        xml_path: Path to the Cobertura XML coverage report.
-
-    Returns:
-        A tuple of (packages_dict, files_dict, overall_line_rate, overall_branch_rate).
-    """
-    tree = ET.parse(xml_path)
-    root = tree.getroot()
-
-    # Get overall coverage from root element
-    overall_line_rate = float(root.get("line-rate", 0))
-    overall_branch_rate = float(root.get("branch-rate", 0))
-
-    packages: dict[str, PackageCoverage] = {}
-    file_stats: dict[str, dict[str, int]] = {}
-
-    for package in root.findall(".//package"):
-        package_path = package.get("name", "unknown")
-
-        line_rate = float(package.get("line-rate", 0))
-        branch_rate = float(package.get("branch-rate", 0))
-
-        # Count lines and branches from classes within this package
-        lines_valid = 0
-        lines_covered = 0
-        branches_valid = 0
-        branches_covered = 0
-
-        for class_elem in package.findall(".//class"):
-            file_path = normalize_coverage_path(class_elem.get("filename", ""))
-            if file_path and file_path not in file_stats:
-                file_stats[file_path] = {
-                    "lines_valid": 0,
-                    "lines_covered": 0,
-                    "branches_valid": 0,
-                    "branches_covered": 0,
-                }
-
-            for line in class_elem.findall(".//line"):
-                lines_valid += 1
-                if int(line.get("hits", 0)) > 0:
-                    lines_covered += 1
-
-                if file_path:
-                    file_stats[file_path]["lines_valid"] += 1
-                    if int(line.get("hits", 0)) > 0:
-                        file_stats[file_path]["lines_covered"] += 1
-
-                # Branch coverage from line elements
-                if line.get("branch") == "true":
-                    condition_coverage = line.get("condition-coverage", "")
-                    if condition_coverage:
-                        # Parse "X% (covered/total)" format
-                        try:
-                            coverage_parts = (
-                                condition_coverage.split("(")[1].rstrip(")").split("/")
-                            )
-                            branches_covered += int(coverage_parts[0])
-                            branches_valid += int(coverage_parts[1])
-                            if file_path:
-                                file_stats[file_path]["branches_covered"] += int(
-                                    coverage_parts[0]
-                                )
-                                file_stats[file_path]["branches_valid"] += int(
-                                    coverage_parts[1]
-                                )
-                        except (IndexError, ValueError):
-                            # Ignore malformed condition-coverage strings; treat this line as having no branch data.
-                            pass
-
-        # Use full package path as the key (no aggregation)
-        packages[package_path] = PackageCoverage(
-            name=package_path,
-            line_rate=line_rate if lines_valid == 0 else lines_covered / lines_valid,
-            branch_rate=branch_rate
-            if branches_valid == 0
-            else branches_covered / branches_valid,
-            lines_valid=lines_valid,
-            lines_covered=lines_covered,
-            branches_valid=branches_valid,
-            branches_covered=branches_covered,
-        )
-
-    files: dict[str, PackageCoverage] = {}
-    for file_path, stats in file_stats.items():
-        lines_valid = stats["lines_valid"]
-        lines_covered = stats["lines_covered"]
-        branches_valid = stats["branches_valid"]
-        branches_covered = stats["branches_covered"]
-
-        files[file_path] = PackageCoverage(
-            name=file_path,
-            line_rate=0 if lines_valid == 0 else lines_covered / lines_valid,
-            branch_rate=0 if branches_valid == 0 else branches_covered / branches_valid,
-            lines_valid=lines_valid,
-            lines_covered=lines_covered,
-            branches_valid=branches_valid,
-            branches_covered=branches_covered,
-        )
-
-    return packages, files, overall_line_rate, overall_branch_rate
-
-
-def format_coverage_value(coverage: float, threshold: float, is_enforced: bool) -> str:
-    """Format a coverage value with optional pass/fail indicator.
-
-    Args:
-        coverage: Coverage percentage (0-100).
-        threshold: Minimum required coverage percentage.
-        is_enforced: Whether this target is enforced.
-
-    Returns:
-        Formatted string like "85.5%" or "85.5% ✅" or "75.0% ❌".
-    """
-    formatted = f"{coverage:.1f}%"
-    if is_enforced:
-        icon = "✅" if coverage >= threshold else "❌"
-        formatted = f"{formatted} {icon}"
-    return formatted
-
-
-def print_coverage_table(
-    packages: dict[str, PackageCoverage],
-    files: dict[str, PackageCoverage],
-    threshold: float,
-    overall_line_rate: float,
-    overall_branch_rate: float,
-) -> None:
-    """Print a formatted coverage summary table.
-
-    Args:
-        packages: Dictionary of package name to coverage data.
-        files: Dictionary of file path to coverage data, used for per-file enforcement.
-        threshold: Minimum required coverage percentage.
-        overall_line_rate: Overall line coverage rate (0-1).
-        overall_branch_rate: Overall branch coverage rate (0-1).
-    """
-    print("\n" + "=" * 80)
-    print("PYTHON TEST COVERAGE REPORT")
-    print("=" * 80)
-
-    # Overall coverage
-    print(f"\nOverall Line Coverage:   {overall_line_rate * 100:.1f}%")
-    print(f"Overall Branch Coverage: {overall_branch_rate * 100:.1f}%")
-    print(f"Threshold:               {threshold}%")
-
-    enforced_targets = {normalize_coverage_path(t) for t in ENFORCED_TARGETS}
-
-    # Package table
-    print("\n" + "-" * 110)
-    print(f"{'Package':<80} {'Lines':<15} {'Line Cov':<15}")
-    print("-" * 110)
-
-    # Sort: enforced package targets first, then alphabetically
-    sorted_packages = sorted(
-        packages.values(),
-        key=lambda p: (p.name not in ENFORCED_TARGETS, p.name),
-    )
-
-    for pkg in sorted_packages:
-        is_enforced = normalize_coverage_path(pkg.name) in enforced_targets
-        enforced_marker = "[ENFORCED] " if is_enforced else ""
-        line_cov = format_coverage_value(
-            pkg.line_coverage_percent, threshold, is_enforced
-        )
-        lines_info = f"{pkg.lines_covered}/{pkg.lines_valid}"
-        package_label = f"{enforced_marker}{pkg.name}"
-
-        print(f"{package_label:<80} {lines_info:<15} {line_cov:<15}")
-
-    print("-" * 110)
-
-    # Enforced file/model entries (if configured)
-    enforced_files = [
-        files[target]
-        for target in sorted(enforced_targets)
-        if target in files and target.endswith(".py")
-    ]
-
-    if enforced_files:
-        print("\nEnforced Files/Models")
-        print("-" * 110)
-        print(f"{'File':<80} {'Lines':<15} {'Line Cov':<15}")
-        print("-" * 110)
-
-        for file_cov in enforced_files:
-            line_cov = format_coverage_value(
-                file_cov.line_coverage_percent, threshold, True
-            )
-            lines_info = f"{file_cov.lines_covered}/{file_cov.lines_valid}"
-            print(f"[ENFORCED] {file_cov.name:<69} {lines_info:<15} {line_cov:<15}")
-
-        print("-" * 110)
-
-
-def check_coverage(xml_path: str, threshold: float) -> bool:
-    """Check if all enforced targets meet the coverage threshold.
-
-    Args:
-        xml_path: Path to the Cobertura XML coverage report.
-        threshold: Minimum required coverage percentage.
-
-    Returns:
-        True if all enforced targets pass, False otherwise.
-    """
-    packages, files, overall_line_rate, overall_branch_rate = parse_coverage_xml(
-        xml_path
-    )
-
-    print_coverage_table(
-        packages, files, threshold, overall_line_rate, overall_branch_rate
-    )
-
-    # Check enforced targets
-    failed_targets: list[str] = []
-    missing_targets: list[str] = []
-
-    for target_name in ENFORCED_TARGETS:
-        normalized_target = normalize_coverage_path(target_name)
-        package_alias = normalized_target.replace("/", ".")
-
-        target_coverage = None
-        if target_name in packages:
-            target_coverage = packages[target_name]
-        elif normalized_target in files:
-            target_coverage = files[normalized_target]
-        elif package_alias in packages:
-            target_coverage = packages[package_alias]
-
-        if target_coverage is None:
-            missing_targets.append(target_name)
-            continue
-
-        if target_coverage.line_coverage_percent < threshold:
-            failed_targets.append(
-                f"{target_name} ({target_coverage.line_coverage_percent:.1f}%)"
-            )
-
-    # Report results
-    if missing_targets:
-        print(
-            f"\n❌ FAILED: Enforced targets not found in coverage report: {', '.join(missing_targets)}"
-        )
-        return False
-
-    if failed_targets:
-        print(
-            f"\n❌ FAILED: The following enforced targets are below {threshold}% coverage threshold:"
-        )
-        for target in failed_targets:
-            print(f"   - {target}")
-        print("\nTo fix: Add more tests to improve coverage for the failing targets.")
-        return False
-
-    if ENFORCED_TARGETS:
-        found_enforced = [
-            target
-            for target in ENFORCED_TARGETS
-            if target in packages or normalize_coverage_path(target) in files
-        ]
-        if found_enforced:
-            print(
-                f"\n✅ PASSED: All enforced targets meet the {threshold}% coverage threshold."
-            )
-
-    return True
-
-
-def main() -> int:
-    """Main entry point.
-
-    Returns:
-        Exit code: 0 for success, 1 for failure.
-    """
-    if len(sys.argv) != 3:
-        print(f"Usage: {sys.argv[0]} <coverage-xml-path> <threshold>")
-        print(f"Example: {sys.argv[0]} python-coverage.xml 85")
-        return 1
-
-    xml_path = sys.argv[1]
-    try:
-        threshold = float(sys.argv[2])
-    except ValueError:
-        print(f"Error: Invalid threshold value: {sys.argv[2]}")
-        return 1
-
-    try:
-        success = check_coverage(xml_path, threshold)
-        return 0 if success else 1
-    except FileNotFoundError:
-        print(f"Error: Coverage file not found: {xml_path}")
-        return 1
-    except ET.ParseError as e:
-        print(f"Error: Failed to parse coverage XML: {e}")
-        return 1
-
-
-if __name__ == "__main__":
-    sys.exit(main())
@@ -12,13 +12,13 @@ env:
  UV_CACHE_DIR: /tmp/.uv-cache

 jobs:
-  pre-commit-hooks:
-    name: Pre-commit Hooks
+  pre-commit:
+    name: Checks
    if: "!cancelled()"
    strategy:
      fail-fast: false
      matrix:
-        python-version: ["3.10"]
+        python-version: ["3.10", "3.14"]
    runs-on: ubuntu-latest
    continue-on-error: true
    defaults:
@@ -37,106 +37,16 @@ jobs:
          python-version: ${{ matrix.python-version }}
          os: ${{ runner.os }}
        env:
+          # Configure a constant location for the uv cache
          UV_CACHE_DIR: /tmp/.uv-cache
      - uses: actions/cache@v5
        with:
-          path: ~/.cache/prek
-          key: prek|${{ matrix.python-version }}|${{ hashFiles('python/.pre-commit-config.yaml') }}
-      - uses: j178/prek-action@v1
-        name: Run Pre-commit Hooks (excluding poe-check)
-        env:
-          SKIP: poe-check
+          path: ~/.cache/pre-commit
+          key: pre-commit|${{ matrix.python-version }}|${{ hashFiles('python/.pre-commit-config.yaml') }}
+      - uses: pre-commit/action@v3.0.1
+        name: Run Pre-Commit Hooks
        with:
-          extra-args: --cd python --all-files
-
-  package-checks:
-    name: Package Checks
-    if: "!cancelled()"
-    strategy:
-      fail-fast: false
-      matrix:
-        python-version: ["3.10"]
-    runs-on: ubuntu-latest
-    continue-on-error: true
-    defaults:
-      run:
-        working-directory: ./python
-    env:
-      UV_PYTHON: ${{ matrix.python-version }}
-    steps:
-      - uses: actions/checkout@v6
-        with:
-          fetch-depth: 0
-      - name: Set up python and install the project
-        id: python-setup
-        uses: ./.github/actions/python-setup
-        with:
-          python-version: ${{ matrix.python-version }}
-          os: ${{ runner.os }}
-        env:
-          UV_CACHE_DIR: /tmp/.uv-cache
-      - name: Run fmt, lint, pyright in parallel across packages
-        run: uv run poe check-packages
-
-  samples-markdown:
-    name: Samples & Markdown
-    if: "!cancelled()"
-    strategy:
-      fail-fast: false
-      matrix:
-        python-version: ["3.10"]
-    runs-on: ubuntu-latest
-    continue-on-error: true
-    defaults:
-      run:
-        working-directory: ./python
-    env:
-      UV_PYTHON: ${{ matrix.python-version }}
-    steps:
-      - uses: actions/checkout@v6
-        with:
-          fetch-depth: 0
-      - name: Set up python and install the project
-        id: python-setup
-        uses: ./.github/actions/python-setup
-        with:
-          python-version: ${{ matrix.python-version }}
-          os: ${{ runner.os }}
-        env:
-          UV_CACHE_DIR: /tmp/.uv-cache
-      - name: Run samples lint
-        run: uv run poe samples-lint
-      - name: Run samples syntax check
-        run: uv run poe samples-syntax
-      - name: Run markdown code lint
-        run: uv run poe markdown-code-lint
-
-  mypy:
-    name: Mypy Checks
-    if: "!cancelled()"
-    strategy:
-      fail-fast: false
-      matrix:
-        python-version: ["3.10"]
-    runs-on: ubuntu-latest
-    continue-on-error: true
-    defaults:
-      run:
-        working-directory: ./python
-    env:
-      UV_PYTHON: ${{ matrix.python-version }}
-    steps:
-      - uses: actions/checkout@v6
-        with:
-          fetch-depth: 0
-      - name: Set up python and install the project
-        id: python-setup
-        uses: ./.github/actions/python-setup
-        with:
-          python-version: ${{ matrix.python-version }}
-          os: ${{ runner.os }}
-        env:
-          UV_CACHE_DIR: /tmp/.uv-cache
+          extra_args: --config python/.pre-commit-config.yaml --all-files
      - name: Run Mypy
        env:
          GITHUB_BASE_REF: ${{ github.event.pull_request.base.ref || github.base_ref || 'main' }}
@@ -2,13 +2,6 @@ name: Python - Merge - Tests

 on:
  workflow_dispatch:
-  workflow_call:
-    inputs:
-      checkout-ref:
-        description: "Git ref to checkout (e.g., a commit SHA from a PR)"
-        required: false
-        type: string
-        default: ""
  pull_request:
    branches: ["main"]
  merge_group:
@@ -36,8 +29,6 @@ jobs:
      pythonChanges: ${{ steps.filter.outputs.python}}
    steps:
      - uses: actions/checkout@v6
-        with:
-          ref: ${{ inputs.checkout-ref }}
      - uses: dorny/paths-filter@v3
        id: filter
        with:
@@ -85,8 +76,6 @@ jobs:
        working-directory: python
    steps:
      - uses: actions/checkout@v6
-        with:
-          ref: ${{ inputs.checkout-ref }}
      - name: Set up python and install the project
        id: python-setup
        uses: ./.github/actions/python-setup
@@ -107,7 +96,8 @@ jobs:
        uses: ./.github/actions/azure-functions-integration-setup
        id: azure-functions-setup
      - name: Test with pytest
-        run: uv run poe all-tests -n logical --dist loadfile --dist worksteal --timeout=120 --session-timeout=900 --timeout_method thread --retries 2 --retry-delay 5
+        timeout-minutes: 10
+        run: uv run poe all-tests -n logical --dist loadfile --dist worksteal --timeout 600 --retries 3 --retry-delay 10
        working-directory: ./python
      - name: Test core samples
        timeout-minutes: 10
@@ -146,8 +136,6 @@ jobs:
        working-directory: python
    steps:
      - uses: actions/checkout@v6
-        with:
-          ref: ${{ inputs.checkout-ref }}
      - name: Set up python and install the project
        id: python-setup
        uses: ./.github/actions/python-setup
@@ -165,8 +153,8 @@ jobs:
          tenant-id: ${{ secrets.AZURE_TENANT_ID }}
          subscription-id: ${{ secrets.AZURE_SUBSCRIPTION_ID }}
      - name: Test with pytest
-        timeout-minutes: 15
-        run: uv run --directory packages/azure-ai poe integration-tests -n logical --dist loadfile --dist worksteal --timeout=120 --session-timeout=900 --timeout_method thread --retries 2 --retry-delay 5
+        timeout-minutes: 10
+        run: uv run --directory packages/azure-ai poe integration-tests -n logical --dist loadfile --dist worksteal --timeout 300 --retries 3 --retry-delay 10
        working-directory: ./python
      - name: Test Azure AI samples
        timeout-minutes: 10
@@ -9,8 +9,6 @@ on:
 env:
  # Configure a constant location for the uv cache
  UV_CACHE_DIR: /tmp/.uv-cache
-  # Coverage threshold percentage for enforced modules
-  COVERAGE_THRESHOLD: 85

 jobs:
  python-tests-coverage:
@@ -39,8 +37,6 @@ jobs:
          UV_CACHE_DIR: /tmp/.uv-cache
      - name: Run all tests with coverage report
        run: uv run poe all-tests-cov --cov-report=xml:python-coverage.xml -q --junitxml=pytest.xml
-      - name: Check coverage threshold
-        run: python ${{ github.workspace }}/.github/workflows/python-check-coverage.py python-coverage.xml ${{ env.COVERAGE_THRESHOLD }}
      - name: Upload coverage report
        uses: actions/upload-artifact@v6
        with:
@@ -199,6 +199,8 @@ temp*/
 .tmp/
 .temp/

+agents.md
+
 # AI
 .claude/
 WARP.md
@@ -207,14 +209,13 @@ WARP.md
 **/tmpclaude*

 # Azurite storage emulator files
-*/__azurite_db_blob__.json*
-*/__azurite_db_blob_extent__.json*
-*/__azurite_db_queue__.json*
-*/__azurite_db_queue_extent__.json*
-*/__azurite_db_table__.json*
+*/__azurite_db_blob__.json
+*/__azurite_db_blob_extent__.json
+*/__azurite_db_queue__.json
+*/__azurite_db_queue_extent__.json
+*/__azurite_db_table__.json
 */__blobstorage__/
 */__queuestorage__/
-*/AzuriteConfig

 # Azure Functions local settings
 local.settings.json
@@ -53,7 +53,7 @@ Still have questions? Join our [weekly office hours](./COMMUNITY.md#public-commu
 ### ✨ **Highlights**

 - **Graph-based Workflows**: Connect agents and deterministic functions using data flows with streaming, checkpointing, human-in-the-loop, and time-travel capabilities
-  - [Python workflows](./python/samples/03-workflows/) | [.NET workflows](./dotnet/samples/GettingStarted/Workflows/)
+  - [Python workflows](./python/samples/getting_started/workflows/) | [.NET workflows](./dotnet/samples/GettingStarted/Workflows/)
 - **AF Labs**: Experimental packages for cutting-edge features including benchmarking, reinforcement learning, and research initiatives
  - [Labs directory](./python/packages/lab/)
 - **DevUI**: Interactive developer UI for agent development, testing, and debugging workflows
@@ -73,11 +73,11 @@ Still have questions? Join our [weekly office hours](./COMMUNITY.md#public-commu
 - **Python and C#/.NET Support**: Full framework support for both Python and C#/.NET implementations with consistent APIs
  - [Python packages](./python/packages/) | [.NET source](./dotnet/src/)
 - **Observability**: Built-in OpenTelemetry integration for distributed tracing, monitoring, and debugging
-  - [Python observability](./python/samples/02-agents/observability/) | [.NET telemetry](./dotnet/samples/GettingStarted/AgentOpenTelemetry/)
+  - [Python observability](./python/samples/getting_started/observability/) | [.NET telemetry](./dotnet/samples/GettingStarted/AgentOpenTelemetry/)
 - **Multiple Agent Provider Support**: Support for various LLM providers with more being added continuously
-  - [Python examples](./python/samples/02-agents/providers/) | [.NET examples](./dotnet/samples/GettingStarted/AgentProviders/)
+  - [Python examples](./python/samples/getting_started/agents/) | [.NET examples](./dotnet/samples/GettingStarted/AgentProviders/)
 - **Middleware**: Flexible middleware system for request/response processing, exception handling, and custom pipelines
-  - [Python middleware](./python/samples/02-agents/middleware/) | [.NET middleware](./dotnet/samples/GettingStarted/Agents/Agent_Step14_Middleware/)
+  - [Python middleware](./python/samples/getting_started/middleware/) | [.NET middleware](./dotnet/samples/GettingStarted/Agents/Agent_Step14_Middleware/)

 ### 💬 **We want your feedback!**

@@ -108,7 +108,7 @@ async def main():
        # api_version=os.environ["AZURE_OPENAI_API_VERSION"],
        # api_key=os.environ["AZURE_OPENAI_API_KEY"],  # Optional if using AzureCliCredential
        credential=AzureCliCredential(), # Optional, if using api_key
-    ).as_agent(
+    ).create_agent(
        name="HaikuBot",
        instructions="You are an upbeat assistant that writes beautifully.",
    )
@@ -125,14 +125,13 @@ Create a simple Agent, using OpenAI Responses, that writes a haiku about the Mic

 ```c#
 // dotnet add package Microsoft.Agents.AI.OpenAI --prerelease
-using Microsoft.Agents.AI;
+using System;
 using OpenAI;
-using OpenAI.Responses;

 // Replace the <apikey> with your OpenAI API key.
 var agent = new OpenAIClient("<apikey>")
-    .GetResponsesClient("gpt-4o-mini")
-    .AsAIAgent(name: "HaikuBot", instructions: "You are an upbeat assistant that writes beautifully.");
+    .GetOpenAIResponseClient("gpt-4o-mini")
+    .CreateAIAgent(name: "HaikuBot", instructions: "You are an upbeat assistant that writes beautifully.");

 Console.WriteLine(await agent.RunAsync("Write a haiku about Microsoft Agent Framework."));
 ```
@@ -143,18 +142,15 @@ Create a simple Agent, using Azure OpenAI Responses with token based auth, that
 // dotnet add package Microsoft.Agents.AI.OpenAI --prerelease
 // dotnet add package Azure.Identity
 // Use `az login` to authenticate with Azure CLI
-using System.ClientModel.Primitives;
-using Azure.Identity;
-using Microsoft.Agents.AI;
+using System;
 using OpenAI;
-using OpenAI.Responses;

 // Replace <resource> and gpt-4o-mini with your Azure OpenAI resource name and deployment name.
 var agent = new OpenAIClient(
    new BearerTokenPolicy(new AzureCliCredential(), "https://ai.azure.com/.default"),
    new OpenAIClientOptions() { Endpoint = new Uri("https://<resource>.openai.azure.com/openai/v1") })
-    .GetResponsesClient("gpt-4o-mini")
-    .AsAIAgent(name: "HaikuBot", instructions: "You are an upbeat assistant that writes beautifully.");
+    .GetOpenAIResponseClient("gpt-4o-mini")
+    .CreateAIAgent(name: "HaikuBot", instructions: "You are an upbeat assistant that writes beautifully.");

 Console.WriteLine(await agent.RunAsync("Write a haiku about Microsoft Agent Framework."));
 ```
@@ -163,9 +159,9 @@ Console.WriteLine(await agent.RunAsync("Write a haiku about Microsoft Agent Fram

 ### Python

- [Getting Started with Agents](./python/samples/01-get-started): progressive tutorial from hello-world to hosting
- [Agent Concepts](./python/samples/02-agents): deep-dive samples by topic (tools, middleware, providers, etc.)
- [Getting Started with Workflows](./python/samples/03-workflows): workflow creation and integration with agents
+- [Getting Started with Agents](./python/samples/getting_started/agents): basic agent creation and tool usage
+- [Chat Client Examples](./python/samples/getting_started/chat_client): direct chat client usage patterns
+- [Getting Started with Workflows](./python/samples/getting_started/workflows): basic workflow creation and integration with agents

 ### .NET

@@ -1,3 +1,3 @@
 # Declarative Agents

-This folder contains sample agent definitions that can be run using the declarative agent support, for python see the [declarative agent python sample folder](../python/samples/02-agents/declarative/).
+This folder contains sample agent definitions that can be run using the declarative agent support, for python see the [declarative agent python sample folder](../python/samples/getting_started/declarative/).
@@ -126,4 +126,4 @@ response = await client.get_response(

 Chosen option: **"Option 2: TypedDict with Generic Type Parameters"**, because it provides full type safety, excellent IDE support with autocompletion, and allows users to extend provider-specific options for their use cases. Extended this Generic to ChatAgents in order to also properly type the options used in agent construction and run methods.

-See [typed_options.py](../../python/samples/02-agents/typed_options.py) for a complete example demonstrating the usage of typed options with custom extensions.
+See [typed_options.py](../../python/samples/getting_started/chat_client/typed_options.py) for a complete example demonstrating the usage of typed options with custom extensions.
@@ -1,423 +0,0 @@
---
-status: accepted
-contact: westey-m
-date: 2025-01-21
-deciders: sergeymenshykh, markwallace, rbarreto, westey-m, stephentoub
-consulted: reubenbond
-informed:
---
-
-# Feature Collections
-
-## Context and Problem Statement
-
-When using agents, we often have cases where we want to pass some arbitrary services or data to an agent or some component in the agent execution stack.
-These services or data are not necessarily known at compile time and can vary by the agent stack that the user has built.
-E.g., there may be an agent decorator or chat client decorator that was added to the stack by the user, and an arbitrary payload needs to be passed to that decorator.
-
-Since these payloads are related to components that are not integral parts of the agent framework, they cannot be added as strongly typed settings to the agent run options.
-However, the payloads could be added to the agent run options as loosely typed 'features', that can be retrieved as needed.
-
-In some cases certain classes of agents may support the same capability, but not all agents do.
-Having the configuration for such a capability on the main abstraction would advertise the functionality to all users, even if their chosen agent does not support it.
-The user may type test for certain agent types, and call overloads on the appropriate agent types, with the strongly typed configuration.
-Having a feature collection though, would be an alternative way of passing such configuration, without needing to type check the agent type.
-All agents that support the functionality would be able to check for the configuration and use it, simplifying the user code.
-If the agent does not support the capability, that configuration would be ignored.
-
-### Sample Scenario 1 - Per Run ChatMessageStore Override for hosting Libraries
-
-We are building an agent hosting library, that can host any agent built using the agent framework.
-Where an agent is not built on a service that uses in-service chat history storage, the hosting library wants to force the agent to use
-the hosting library's chat history storage implementation.
-This chat history storage implementation may be specifically tailored to the type of protocol that the hosting library uses, e.g. conversation id based storage or response id based storage.
-The hosting library does not know what type of agent it is hosting, so it cannot provide a strongly typed parameter on the agent.
-Instead, it adds the chat history storage implementation to a feature collection, and if the agent supports custom chat history storage, it retrieves the implementation from the feature collection and uses it.
-
-```csharp
-// Pseudo-code for an agent hosting library that supports conversation id based hosting.
-public async Task<string> HandleConversationsBasedRequestAsync(AIAgent agent, string conversationId, string userInput)
-{
-    var thread = await this._threadStore.GetOrCreateThread(conversationId);
-
-    // The hosting library can set a per-run chat message store via Features that only applies for that run.
-    // This message store will load and save messages under the conversation id provided.
-    ConversationsChatMessageStore messageStore = new(this._dbClient, conversationId);
-    var response = await agent.RunAsync(
-        userInput,
-        thread,
-        options: new AgentRunOptions()
-        {
-            Features = new AgentFeatureCollection().WithFeature<ChatMessageStore>(messageStore)
-        });
-
-    await this._threadStore.SaveThreadAsync(conversationId, thread);
-    return response.Text;
-}
-
-// Pseudo-code for an agent hosting library that supports response id based hosting.
-public async Task<(string responseMessage, string responseId)> HandleResponseIdBasedRequestAsync(AIAgent agent, string previousResponseId, string userInput)
-{
-    var thread = await this._threadStore.GetOrCreateThreadAsync(previousResponseId);
-
-    // The hosting library can set a per-run chat message store via Features that only applies for that run.
-    // This message store will buffer newly added messages until explicitly saved after the run.
-    ResponsesChatMessageStore messageStore = new(this._dbClient, previousResponseId);
-
-    var response = await agent.RunAsync(
-        userInput,
-        thread,
-        options: new AgentRunOptions()
-        {
-            Features = new AgentFeatureCollection().WithFeature<ChatMessageStore>(messageStore)
-        });
-
-    // Since the message store may not actually have been used at all (if the agent's underlying chat client requires service-based chat history storage),
-    // we may not have anything to save back to the database.
-    // We still want to generate a new response id though, so that we can save the updated thread state under that id.
-    // We should also use the same id to save any buffered messages in the message store if there are any.
-    var newResponseId = this.GenerateResponseId();
-    if (messageStore.HasBufferedMessages)
-    {
-        await messageStore.SaveBufferedMessagesAsync(newResponseId);
-    }
-    
-    // Save the updated thread state under the new response id that was generated by the store.
-    await this._threadStore.SaveThreadAsync(newResponseId, thread);
-    return (response.Text, newResponseId);
-}
-```
-
-### Sample Scenario 2 - Structured output
-
-Currently our base abstraction does not support structured output, since the capability is not supported by all agents.
-For those agents that don't support structured output, we could add an agent decorator that takes the response from the underlying agent, and applies structured output parsing on top of it via an additional LLM call.
-
-If we add structured output configuration as a feature, then any agent that supports structured output could retrieve the configuration from the feature collection and apply it, and where it is not supported, the configuration would simply be ignored.
-
-We could add a simple StructuredOutputAgentFeature that can be added to the list of features and also be used to return the generated structured output.
-
-```csharp
-internal class StructuredOutputAgentFeature
-{
-    public Type? OutputType { get; set; }
-
-    public JsonSerializerOptions? SerializerOptions { get; set; }
-
-    public bool? UseJsonSchemaResponseFormat { get; set; }
-
-    // Contains the result of the structured output parsing request.
-    public ChatResponse? ChatResponse { get; set; }
-}
-```
-
-We can add a simple decorator class that does the chat client invocation.
-
-```csharp
-public class StructuredOutputAgent : DelegatingAIAgent
-{
-    private readonly IChatClient _chatClient;
-    public StructuredOutputAgent(AIAgent innerAgent, IChatClient chatClient)
-        : base(innerAgent)
-    {
-        this._chatClient = Throw.IfNull(chatClient);
-    }
-
-    public override async Task<AgentRunResponse> RunAsync(
-        IEnumerable<ChatMessage> messages,
-        AgentThread? thread = null,
-        AgentRunOptions? options = null,
-        CancellationToken cancellationToken = default)
-    {
-        // Run the inner agent first, to get back the text response we want to convert.
-        var response = await base.RunAsync(messages, thread, options, cancellationToken).ConfigureAwait(false);
-
-        if (options?.Features?.TryGet<StructuredOutputAgentFeature>(out var responseFormatFeature) is true
-            && responseFormatFeature.OutputType is not null)
-        {
-            // Create the chat options to request structured output.
-            ChatOptions chatOptions = new()
-            {
-                ResponseFormat = ChatResponseFormat.ForJsonSchema(responseFormatFeature.OutputType, responseFormatFeature.SerializerOptions)
-            };
-
-            // Invoke the chat client to transform the text output into structured data.
-            // The feature is updated with the result.
-            // The code can be simplified by adding a non-generic structured output GetResponseAsync
-            // overload that takes Type as input.
-            responseFormatFeature.ChatResponse = await this._chatClient.GetResponseAsync(
-                messages: new[]
-                {
-                    new ChatMessage(ChatRole.System, "You are a json expert and when provided with any text, will convert it to the requested json format."),
-                    new ChatMessage(ChatRole.User, response.Text)
-                },
-                options: chatOptions,
-                cancellationToken: cancellationToken).ConfigureAwait(false);
-        }
-
-        return response;
-    }
-}
-```
-
-Finally, we can add an extension method on `AIAgent` that can add the feature to the run options and check the feature for the structured output result and add the deserialized result to the response.
-
-```csharp
-public static async Task<AgentRunResponse<T>> RunAsync<T>(
-    this AIAgent agent,
-    IEnumerable<ChatMessage> messages,
-    AgentThread? thread = null,
-    JsonSerializerOptions? serializerOptions = null,
-    AgentRunOptions? options = null,
-    bool? useJsonSchemaResponseFormat = null,
-    CancellationToken cancellationToken = default)
-{
-    // Create the structured output feature.
-    var structuredOutputFeature = new StructuredOutputAgentFeature();
-    structuredOutputFeature.OutputType = typeof(T);
-    structuredOutputFeature.UseJsonSchemaResponseFormat = useJsonSchemaResponseFormat;
-
-    // Run the agent.
-    options ??= new AgentRunOptions();
-    options.Features ??= new AgentFeatureCollection();
-    options.Features.Set(structuredOutputFeature);
-
-    var response = await agent.RunAsync(messages, thread, options, cancellationToken).ConfigureAwait(false);
-
-    // Deserialize the JSON output.
-    if (structuredOutputFeature.ChatResponse is not null)
-    {
-        var typed = new ChatResponse<T>(structuredOutputFeature.ChatResponse, serializerOptions ?? AgentJsonUtilities.DefaultOptions);
-        return new AgentRunResponse<T>(response, typed.Result);
-    }
-
-    throw new InvalidOperationException("No structured output response was generated by the agent.");
-}
-```
-
-We can then use the extension method with any agent that supports structured output or that has
-been decorated with the `StructuredOutputAgent` decorator.
-
-```csharp
-agent = new StructuredOutputAgent(agent, chatClient);
-
-AgentRunResponse<PersonInfo> response = await agent.RunAsync<PersonInfo>([new ChatMessage(
-    ChatRole.User,
-    "Please provide information about John Smith, who is a 35-year-old software engineer.")]);
-```
-
-## Implementation Options
-
-Three options were considered for implementing feature collections:
-
- **Option 1**: FeatureCollections similar to ASP.NET Core
- **Option 2**: AdditionalProperties Dictionary
- **Option 3**: IServiceProvider
-
-Here are some comparisons about their suitability for our use case:
-
-| Criteria         | Feature Collection | Additional Properties | IServiceProvider |
-|------------------|--------------------|-----------------------|------------------|
-|Ease of use       |✅ Good             |❌ Bad                |✅ Good           |
-|User familiarity  |❌ Bad              |✅ Good               |✅ Good           |
-|Type safety       |✅ Good             |❌ Bad                |✅ Good           |
-|Ability to modify registered options when progressing down the stack|✅ Supported|✅ Supported|❌ Not-Supported (IServiceProvider is read-only)|
-|Already available in MEAI stack|❌ No|✅ Yes|❌ No|
-|Ambiguity with existing AdditionalProperties|❌ Yes|✅ No|❌ Yes|
-
-## IServiceProvider
-
-Service Collections and Service Providers provide a very popular way to register and retrieve services by type and could be used as a way to pass features to agents and chat clients.
-
-However, since IServiceProvider is read-only, it is not possible to modify the registered services when progressing down the execution stack.
-E.g. an agent decorator cannot add additional services to the IServiceProvider passed to it when calling into the inner agent.
-
-IServiceProvider also does not expose a way to list all services contained in it, making it difficult to copy services from one provider to another.
-
-This lack of mutability makes IServiceProvider unsuitable for our use case, since we will not be able to use it to build sample scenario 2.
-
-## AdditionalProperties dictionary
-
-The AdditionalProperties dictionary is already available on various options classes in the agent framework as well as in the MEAI stack and
-allows storing arbitrary key/value pairs, where the key is a string and the value is an object.
-
-While FeatureCollection uses Type as a key, AdditionalProperties uses string keys.
-This means that users need to agree on string keys to use for specific features, however it is also possible to use Type.FullName as a key by convention
-to avoid key collisions, which is an easy convention to follow.
-
-Since the value of AdditionalProperties is of type object, users need to cast the value to the expected type when retrieving it, which is also
-a drawback, but when using the convention of using Type.FullName as a key, there is at least a clear expectation of what type to cast to.
-
-```csharp
-// Setting a feature
-options.AdditionalProperties[typeof(MyFeature).FullName] = new MyFeature();
-
-// Retrieving a feature
-if (options.AdditionalProperties.TryGetValue(typeof(MyFeature).FullName, out var featureObj)
-    && featureObj is MyFeature myFeature)
-{
-    // Use myFeature
-}
-```
-
-It would also be possible to add extension methods to simplify setting and getting features from AdditionalProperties.
-Having a base class for features should help make this more feature rich.
-
-```csharp
-// Setting a feature, this can use Type.FullName as the key.
-options.AdditionalProperties
-    .WithFeature(new MyFeature());
-
-// Retrieving a feature, this can use Type.FullName as the key.
-if (options.AdditionalProperties.TryGetFeature<MyFeature>(out var myFeature))
-{
-    // Use myFeature
-}
-```
-
-It would also be possible to add extension methods for a feature to simplify setting and getting features from AdditionalProperties.
-
-```csharp
-// Setting a feature
-options.AdditionalProperties
-    .WithMyFeature(new MyFeature());
-// Retrieving a feature
-if (options.AdditionalProperties.TryGetMyFeature(out var myFeature))
-{
-    // Use myFeature
-}
-```
-
-## Feature Collection
-
-If we choose the feature collection option, we need to decide on the design of the feature collection itself.
-
-### Feature Collections extension points
-
-We need to decide the set of actions that feature collections would be supported for. Here is the suggested list of actions:
-
-**MAAI.AIAgent:**
-
-1. GetNewThread
-    1. E.g. this would allow passing an already existing storage id for the thread to use, or an initialized custom chat message store to use.
-1. DeserializeThread
-    1. E.g. this would allow passing an already existing storage id for the thread to use, or an initialized custom chat message store to use.
-1. Run / RunStreaming
-    1. E.g. this would allow passing an override chat message store just for that run, or a desired schema for a structured output middleware component.
-
-**MEAI.ChatClient:**
-
-1. GetResponse / GetStreamingResponse
-
-### Reconciling with existing AdditionalProperties
-
-If we decide to add feature collections, separately from the existing AdditionalProperties dictionaries, we need to consider how to explain to users when to use each one.
-One possible approach though is to have the one use the other under the hood.
-AdditionalProperties could be stored as a feature in the feature collection.
-
-Users would be able to retrieve additional properties from the feature collection, in addition to retrieving it via a dedicated AdditionalProperties property.
-E.g. `features.Get<AdditionalPropertiesDictionary>()`
-
-One challenge with this approach is that when setting a value in the AdditionalProperties dictionary, the feature collection would need to be created first if it does not already exist.
-
-```csharp
-public class AgentRunOptions
-{
-    public AdditionalPropertiesDictionary? AdditionalProperties { get; set; }
-    public IAgentFeatureCollection? Features { get; set; }
-}
-
-var options = new AgentRunOptions();
-// This would need to create the feature collection first, if it does not already exist.
-options.AdditionalProperties ??= new AdditionalPropertiesDictionary();
-```
-
-Since IAgentFeatureCollection is an interface, AgentRunOptions would need to have a concrete implementation of the interface to create, meaning that the user cannot decide.
-It also means that if the user doesn't realise that AdditionalProperties is implemented using feature collections, they may set a value on AdditionalProperties, and then later overwrite the entire feature collection, losing the AdditionalProperties feature.
-
-Options to avoid these issues:
-
-1. Make `Features` readonly.
-    1. This would prevent the user from overwriting the feature collection after setting AdditionalProperties.
-    1. Since the user cannot set their own implementation of IAgentFeatureCollection, having an interface for it may not be necessary.
-
-### Feature Collection Implementation
-
-We have two options for implementing feature collections:
-
-1. Create our own [IAgentFeatureCollection interface](https://github.com/microsoft/agent-framework/pull/2354/files#diff-9c42f3e60d70a791af9841d9214e038c6de3eebfc10e3997cb4cdffeb2f1246d) and [implementation](https://github.com/microsoft/agent-framework/pull/2354/files#diff-a435cc738baec500b8799f7f58c1538e3bb06c772a208afc2615ff90ada3f4ca).
-2. Reuse the asp.net [IFeatureCollection interface](https://github.com/dotnet/aspnetcore/blob/main/src/Extensions/Features/src/IFeatureCollection.cs) and [implementation](https://github.com/dotnet/aspnetcore/blob/main/src/Extensions/Features/src/FeatureCollection.cs).
-
-#### Roll our own
-
-Advantages:
-
-Creating our own IAgentFeatureCollection interface and implementation has the advantage of being more clearly associated with the agent framework and allows us to
-improve on some of the design decisions made in asp.net core's IFeatureCollection.
-
-Drawbacks:
-
-It would mean a different implementation to maintain and test.
-
-#### Reuse asp.net IFeatureCollection
-
-Advantages:
-
-Reusing the asp.net IFeatureCollection has the advantage of being able to reuse the well-established and tested implementation from asp.net
-core. Users who are using agents in an asp.net core application may be able to pass feature collections from asp.net core to the agent framework directly.
-
-Drawbacks:
-
-While the package name is `Microsoft.Extensions.Features`, the namespaces of the types are `Microsoft.AspNetCore.Http.Features`, which may create confusion for users of agent framework who are not building web applications or services.
-Users may rightly ask: Why do I need to use a class from asp.net core when I'm not building a web application / service?
-
-The current design has some design issues that would be good to avoid.  E.g. it does not distinguish between a feature being "not set" and "null". Get returns both as null and there is no tryget method.
-Since the [default implementation](https://github.com/dotnet/aspnetcore/blob/main/src/Extensions/Features/src/FeatureCollection.cs) also supports value types, it throws for null values of value types.
-A TryGet method would be more appropriate.
-
-## Feature Layering
-
-One possible scenario when adding support for feature collections is to allow layering of features by scope.
-
-The following levels of scope could be supported:
-
-1. Application - Application wide features that apply to all agents / chat clients
-2. Artifact (Agent / ChatClient) - Features that apply to all runs of a specific agent or chat client instance
-3. Action (GetNewThread / Run / GetResponse) - Feature that apply to a single action only
-
-When retrieving a feature from the collection, the search would start from the most specific scope (Action) and progress to the least specific scope (Application), returning the first matching feature found.
-
-Introducing layering adds some challenges:
-
- There may be multiple feature collections at the same scope level, e.g. an Agent that uses a ChatClient where both have their own feature collections.
-  - Do we layer the agent feature collection over the chat client feature collection (Application -> ChatClient -> Agent -> Run), or only use the agent feature collection in the agent (Application -> Agent -> Run), and the chat client feature collection in the chat client (Application -> ChatClient -> Run)?
- The appropriate base feature collection may change when progressing down the stack, e.g. when an Agent calls a ChatClient, the action feature collection stays the same, but the artifact feature collection changes.
- Who creates the feature collection hierarchy?
-  - Since the hierarchy changes as it progresses down the execution stack, and the caller can only pass in the action level feature collection, the callee needs to combine it with its own artifact level feature collection and the application level feature collection. Each action will need to build the appropriate feature collection hierarchy, at the start of its execution.
- For Artifact level features, it seems odd to pass them in as a bag of untyped features, when we are constructing a known artifact type and therefore can have typed settings.
-  - E.g. today we have a strongly typed setting on ChatClientAgentOptions to configure a ChatMessageStore for the agent.
- To avoid global statics for application level features, the user would need to pass in the application level feature collection to each artifact that they create.
-  - This would be very odd if the user also already has to strongly typed settings for each feature that they want to set at the artifact level.
-
-### Layering Options
-
-1. No layering - only a single feature collection is supported per action (the caller can still create a layered collection if desired, but the callee does not do any layering automatically).
-    1. Fallback is to any features configured on the artifact via strongly typed settings.
-1. Full layering - support layering at all levels (Application -> Artifact -> Action).
-    1. Only apply applicable artifact level features when calling into that artifact.
-    1. Apply upstream artifact features when calling into downstream artifacts, e.g. Feature hierarchy in ChatClientAgent would be `Application -> Agent -> Run` and in ChatClient would be `Application -> ChatClient -> Agent -> Run` or `Application -> Agent -> ChatClient -> Run`
-    1. The user needs to provide the application level feature collection to each artifact that they create and artifact features are passed via strongly typed settings.
-
-### Accessing application level features Options
-
-We need to consider how application level features would be accessed if supported.
-
-1. The user provides the application level feature collection to each artifact that the user constructs
-    1. Passing the application level feature collection to each artifact is tedious for the user.
-1. There is a static application level feature collection that can be accessed globally.
-    1. Statics create issues with testing and isolation.
-
-## Decisions
-
- Feature Collections Container: Use AdditionalProperties
- Feature Layering: No layering - only a single collection/dictionary is supported per action. Application layers can be added later if needed.
@@ -1,147 +0,0 @@
---
-status: proposed
-contact: westey-m
-date: 2026-01-27
-deciders: sergeymenshykh, markwallace, rbarreto, dmytrostruk, westey-m, eavanvalkenburg, stephentoub, lokitoth, alliscode, taochenosu, moonbox3
-consulted: 
-informed: 
---
-
-# AgentRunContext for Agent Run
-
-## Context and Problem Statement
-
-During an agent run, various components involved in the execution (middleware, filters, tools, nested agents, etc.) may need access to contextual information about the current run, such as:
-
-1. The agent that is executing the run
-2. The session associated with the run
-3. The request messages passed to the agent
-4. The run options controlling the agent's behavior
-
-Additionally, some components may need to modify this context during execution, for example:
-
- Replacing the session with a different one
- Modifying the request messages before they reach the agent core
- Updating or replacing the run options entirely
-
-Currently, there is no standardized way to access or modify this context from arbitrary code that executes during an agent run, especially from deeply nested call stacks where the context is not explicitly passed.
-
-## Sample Scenario
-
-When using an Agent as an AIFunction developers may want to pass context from the parent agent run to the child agent run. For example, the developer may want to copy chat history to the child agent, or share the same session across both agents.
-
-To enable these scenarios, we need a way to access the parent agent run context, including e.g. the parent agent itself, the parent agent session, and the parent run options from function tool calls.
-
-```csharp
-    public static AIFunction AsAIFunctionWithSessionPropagation(this ChatClientAgent agent, AIFunctionFactoryOptions? options = null)
-    {
-        Throw.IfNull(agent);
-
-        [Description("Invoke an agent to retrieve some information.")]
-        async Task<string> InvokeAgentAsync(
-            [Description("Input query to invoke the agent.")] string query,
-            CancellationToken cancellationToken)
-        {
-            // Get the session from the parent agent and pass it to the child agent.
-            var session = AIAgent.CurrentRunContext?.Session;
-
-            // Alternatively, the developer may want to create a new session but copy over the chat history from the parent agent.
-            // var parentChatHistory = AIAgent.CurrentRunContext?.Session?.GetService<IList<ChatMessage>>();
-            // if (parentChatHistory != null)
-            // {
-            //     var chp = new InMemoryChatHistoryProvider();
-            //     foreach (var message in parentChatHistory)
-            //     {
-            //         chp.Add(message);
-            //     }
-            //     session = agent.GetNewSession(chp);
-            // }
-
-            var response = await agent.RunAsync(query, session: session, cancellationToken: cancellationToken).ConfigureAwait(false);
-            return response.Text;
-        }
-
-        options ??= new();
-        options.Name ??= SanitizeAgentName(agent.Name);
-        options.Description ??= agent.Description;
-
-        return AIFunctionFactory.Create(InvokeAgentAsync, options);
-    }
-```
-
-## Decision Drivers
-
- Components executing during an agent run need access to run context without explicit parameter passing through every layer
- Context should flow naturally across async calls without manual propagation
- The design should allow modification of context properties by agent decorators (e.g., replacing options or session)
- Solution should be consistent with patterns used in similar frameworks (e.g., `FunctionInvokingChatClient.CurrentContext` `HttpContext.Current`, `Activity.Current`)
-
-## Considered Options
-
- **Option 1**: Pass context explicitly through all method signatures
- **Option 2**: Use `AsyncLocal<T>` to provide ambient context accessible anywhere during the run
- **Option 3**: Use a combination of explicit parameters for `RunCoreAsync` and `AsyncLocal<T>` for ambient access
-
-## Decision Outcome
-
-Chosen option: **Option 3** - Combination of explicit parameters and AsyncLocal ambient access.
-
-This approach provides the best of both worlds:
-
-1. **Explicit parameters are passed to `RunCoreAsync`**: The core agent implementation receives the parameters explicitly, making it clear what data is available and enabling easy unit testing. Any modification of these in a decorator will require calling `RunAsync` on the inner agent with the updated parameters, which would result in the inner agent creating a new `AgentRunContext` instance.
-
-   ```csharp
-    public async Task<AgentResponse> RunAsync(
-        IEnumerable<ChatMessage> messages,
-        AgentSession? session = null,
-        AgentRunOptions? options = null,
-        CancellationToken cancellationToken = default)
-    {
-
-        CurrentRunContext = new(this, session, messages as IReadOnlyCollection<ChatMessage> ?? messages.ToList(), options);
-        return await this.RunCoreAsync(messages, session, options, cancellationToken).ConfigureAwait(false);
-    }
-   ```
-
-2. **`AsyncLocal<AgentRunContext?>` for ambient access**: The context is stored in an `AsyncLocal<T>` field, making it accessible from any code executing during the agent run via a static property.
-
-    The main scenario for this is to allow deeply nested components (e.g., tools, chat client middleware) to access the context without needing to pass it through every method signature. These are external components that cannot easily be modified to accept additional parameters. For internal components, we prefer passing any parameters explicitly.
-
-   ```csharp
-   public static AgentRunContext? CurrentRunContext
-   {
-       get => s_currentContext.Value;
-       protected set => s_currentContext.Value = value;
-   }
-   ```
-
-### AgentRunContext Design
-
-The `AgentRunContext` class encapsulates all run-related state:
-
-```csharp
-public class AgentRunContext
-{
-    public AgentRunContext(
-        AIAgent agent,
-        AgentSession? session,
-        IReadOnlyCollection<ChatMessage> requestMessages,
-        AgentRunOptions? agentRunOptions)
-
-    public AIAgent Agent { get; }
-    public AgentSession? Session { get; }
-    public IReadOnlyCollection<ChatMessage> RequestMessages { get; }
-    public AgentRunOptions? RunOptions { get; }
-}
-```
-
-Key design decisions:
-
- **All properties are read-only**: While some of the sub-properties on the provided properties (like `AgentRunOptions.AllowBackgroundResponses`) may be mutable, the `AgentRunContext` itself is immutable and we want to discourage anyone modifying the values in the context.  Modifying the context is unlikely to result in the desired behavior, as the values will typically already have been used by the time any custom code accesses them.
-
-### Benefits
-
-1. **Ambient Access**: Any code executing during the run can access context via `AIAgent.CurrentRunContext` without needing explicit parameters
-2. **Async Flow**: `AsyncLocal<T>` automatically flows across async/await boundaries
-3. **Modifiability**: Components can modify or replace session, messages, or options as needed
-4. **Testability**: The explicit parameter to `RunCoreAsync` makes unit testing straightforward
@@ -1,658 +0,0 @@
---
-status: proposed
-contact: sergeymenshykh
-date: 2026-01-22
-deciders: rbarreto, westey-m, stephentoub
-informed: {}
---
-
-# Structured Output
-
-Structured output is a valuable aspect of any agent system, since it forces an agent to produce output in a required format that may include required fields.
-This allows easily turning unstructured data into structured data using a general-purpose language model.
-
-## Context and Problem Statement
-
-Structured output is currently supported only by `ChatClientAgent` and can be configured in two ways:
-
-**Approach 1: ResponseFormat + Deserialize**
-
-Specify the SO type schema via the `ChatClientAgent{Run}Options.ChatOptions.ResponseFormat` property at agent creation or invocation time, then use `JsonSerializer.Deserialize<T>` to extract the structured data from the response text.
-
-	```csharp
-	// SO type can be provided at agent creation time
-	ChatClientAgent agent = chatClient.AsAIAgent(new ChatClientAgentOptions()
-	{
-		Name = "...",
-		ChatOptions = new() { ResponseFormat = ChatResponseFormat.ForJsonSchema<PersonInfo>() }
-	});
-
-	AgentResponse response = await agent.RunAsync("...");
-
-	PersonInfo personInfo = response.Deserialize<PersonInfo>(JsonSerializerOptions.Web);
-
-	Console.WriteLine($"Name: {personInfo.Name}");
-	Console.WriteLine($"Age: {personInfo.Age}");
-	Console.WriteLine($"Occupation: {personInfo.Occupation}");
-
-	// Alternatively, SO type can be provided at agent invocation time
-	response = await agent.RunAsync("...", new ChatClientAgentRunOptions()
-	{
-		ChatOptions = new() { ResponseFormat = ChatResponseFormat.ForJsonSchema<PersonInfo>() }
-	});
-
-	personInfo = response.Deserialize<PersonInfo>(JsonSerializerOptions.Web);
-
-	Console.WriteLine($"Name: {personInfo.Name}");
-	Console.WriteLine($"Age: {personInfo.Age}");
-	Console.WriteLine($"Occupation: {personInfo.Occupation}");
-	```
-
-**Approach 2: Generic RunAsync<T>**
-
-Supply the SO type as a generic parameter to `RunAsync<T>` and access the parsed result directly via the `Result` property.
-
-	```csharp
-	ChatClientAgent agent = ...;
-	
-	AgentResponse<PersonInfo> response = await agent.RunAsync<PersonInfo>("...");
-
-	Console.WriteLine($"Name: {response.Result.Name}");
-	Console.WriteLine($"Age: {response.Result.Age}");
-	Console.WriteLine($"Occupation: {response.Result.Occupation}");
-	```
-	Note: `RunAsync<T>` is an instance method of `ChatClientAgent` and not part of the `AIAgent` base class since not all agents support structured output.
-
-Approach 1 is perceived as cumbersome by the community, as it requires additional effort when using primitive or collection types - the SO schema may need to be wrapped in an artificial JSON object. Otherwise, the caller will encounter an error like _Invalid schema for response_format 'Movie': schema must be a JSON Schema of 'type: "object"', got 'type: "array"'_. 
-This occurs because OpenAI and compatible APIs require a JSON object as the root schema.
-
-Approach 1 is also necessary in scenarios where (a) agents can only be configured with SO at creation time (such as with `AIProjectClient`), (b) the SO type is not known at compile time, or (c) the JSON schema is represented as text (for declarative agents) or as a `JsonElement`.
-
-Approach 2 is more convenient and works seamlessly with primitives and collections. However, it requires the SO type to be known at compile time, making it less flexible.
-
-Additionally, since the `RunAsync<T>` methods are instance methods of `ChatClientAgent` and are not part of the `AIAgent` base class, applying decorators like `OpenTelemetryAgent` on top of `ChatClientAgent` prevents users from accessing `RunAsync<T>`, meaning structured output is not available with decorated agents.
-
-Given the different scenarios above in which structured output can be used, there is no one-size-fits-all solution. Each approach has its own advantages and limitations,
-and the two can complement each other to provide a comprehensive structured output experience across various use cases.
-
-## Approaches Overview
-
-1. SO usage via `ResponseFormat` property
-2. SO usage via `RunAsync<T>` generic method
-
-## 1. SO usage via `ResponseFormat` property
-
-This approach should be used in the following scenarios:
- - 1.1 SO result as text is sufficient as is, and deserialization is not required
- - 1.2 SO for inter-agent collaboration
- - 1.3 SO can only be configured at agent creation time (such as with `AIProjectClient`)
- - 1.4 SO type is not known at compile time and represented by System.Type
- - 1.5 SO is represented by JSON schema and there's no corresponding .NET type either at compile time or at runtime
- - 1.6 SO in streaming scenarios, where the SO response is produced in parts
-
-**Note: Primitives and arrays are not supported by this approach.**
-
-When a caller provides a schema via `ResponseFormat`, they are explicitly telling the framework what schema to use. The framework passes that schema through as-is and
-is not responsible for transforming it. Because the framework does not own the schema, it cannot wrap primitives or arrays into a JSON object to satisfy API requirements,
-nor can it unwrap the response afterward - the caller controls the schema and is responsible for ensuring it is compatible with the underlying API.
-
-This is in contrast to the `RunAsync<T>` approach (section 2), where the caller provides a type `T` and says "make it work." In that case, the caller does not
-dictate the schema - the framework infers the schema from `T`, owns the end-to-end pipeline (schema generation, API invocation, and deserialization), and can
-therefore wrap and unwrap primitives and arrays transparently.
-
-Additionally, in streaming scenarios (1.6), the framework cannot reliably unwrap a response it did not wrap, since it has no way of knowing whether the caller wrapped the schema.Wrapping and unwrapping can only be done safely when the framework owns the entire lifecycle - from schema creation through deserialization — which is only the case with `RunAsync<T>`.
-
-If a caller needs to work with primitives or arrays via the `ResponseFormat` approach, they can easily create a wrapper type around them:
-
-```csharp
-public class MovieListWrapper
-{
-    public List<string> Movies { get; set; }
-}
-```
-
-### 1.1 SO result as text is sufficient as is, and deserialization is not required
-
-In this scenario, the caller only needs the raw JSON text returned by the model and does not need to deserialize it into a .NET type.
-The SO schema is specified via `ResponseFormat` at agent creation or invocation time, and the response text is consumed directly from the `AgentResponse`.
-
-```csharp
-AIAgent agent = chatClient.AsAIAgent();
-
-AgentRunOptions runOptions = new()
-{
-        ResponseFormat = ChatResponseFormat.ForJsonSchema<PersonInfo>()
-};
-
-AgentResponse response = await agent.RunAsync("...", options: runOptions);
-
-Console.WriteLine(response.Text);
-```
-
-### 1.2 SO for inter-agent collaboration
-
-This scenario assumes a multi-agent setup where agents collaborate by passing messages to each other.
-One agent produces structured output as text that is then passed directly as input to the next agent, without intermediate deserialization.
-
-```csharp
-// First agent extracts structured data from unstructured input
-AIAgent extractionAgent = chatClient.AsAIAgent(new ChatClientAgentOptions()
-{
-    Name = "ExtractionAgent",
-    ChatOptions = new() 
-    { 
-        Instructions = "Extract person information from the provided text.",
-        ResponseFormat = ChatResponseFormat.ForJsonSchema<PersonInfo>() 
-    }
-});
-
-AgentResponse extractionResponse = await extractionAgent.RunAsync("John Smith is a 35-year-old software engineer.");
-
-// Pass the message with structured output text directly to the next agent
-ChatMessage soMessage = extractionResponse.Messages.Last();
-
-AIAgent summaryAgent = chatClient.AsAIAgent(new ChatClientAgentOptions()
-{
-    Name = "SummaryAgent",
-    ChatOptions = new() { Instructions = "Given the following structured person data, write a short professional bio." }
-});
-
-AgentResponse summaryResponse = await summaryAgent.RunAsync(soMessage);
-
-Console.WriteLine(summaryResponse);
-```
-
-### 1.3 SO configured at agent creation time
-
-In this scenario, the SO schema can only be configured at agent creation time (such as with `AIProjectClient`) and cannot be changed on a per-run basis.
-The caller specifies the `ResponseFormat` when creating the agent, and all subsequent invocations use the same schema.
-
-```csharp
-AIProjectClient client = ...;
-
-AIAgent agent = await client.CreateAIAgentAsync(model: "<model>", new ChatClientAgentOptions()
-{
-    Name = "...",
-    ChatOptions = new() { ResponseFormat = ChatResponseFormat.ForJsonSchema<PersonInfo>() }
-});
-
-AgentResponse response = await agent.RunAsync("Please provide information about John Smith.");
-
-PersonInfo personInfo = JsonSerializer.Deserialize<PersonInfo>(response.Text, JsonSerializerOptions.Web)!;
-
-Console.WriteLine($"Name: {personInfo.Name}");
-Console.WriteLine($"Age: {personInfo.Age}");
-Console.WriteLine($"Occupation: {personInfo.Occupation}");
-```
-
-### 1.4 SO type not known at compile time and represented by System.Type
-
-In this scenario, the SO type is not known at compile time and is provided as a `System.Type` at runtime. This is useful for dynamic scenarios where the schema is determined programmatically, 
-such as when building tooling or frameworks that work with user-defined types.
-
-```csharp
-Type soType = GetStructuredOutputTypeFromConfiguration(); // e.g., typeof(PersonInfo)
-
-ChatResponseFormat responseFormat = ChatResponseFormat.ForJsonSchema(soType);
-
-AgentResponse response = await agent.RunAsync("...", new ChatClientAgentRunOptions()
-{
-    ChatOptions = new() { ResponseFormat = responseFormat }
-});
-
-PersonInfo personInfo = (PersonInfo)JsonSerializer.Deserialize(response.Text, soType, JsonSerializerOptions.Web)!;
-```
-
-### 1.5 SO represented by JSON schema with no corresponding .NET type
-
-In this scenario, the SO schema is represented as raw JSON schema text or a `JsonElement`, and there is no corresponding .NET type available at compile time or runtime.
-This is typical for declarative agents or scenarios where schemas are loaded from external configuration.
-
-```csharp
-// JSON schema provided as a string, e.g., loaded from a configuration file
-string jsonSchema = """
-{
-    "type": "object",
-    "properties": {
-        "name": { "type": "string" },
-        "age": { "type": "integer" },
-        "occupation": { "type": "string" }
-    },
-    "required": ["name", "age", "occupation"]
-}
-""";
-
-ChatResponseFormat responseFormat = ChatResponseFormat.ForJsonSchema(
-    jsonSchemaName: "PersonInfo",
-    jsonSchema: BinaryData.FromString(jsonSchema));
-
-AgentResponse response = await agent.RunAsync("...", new ChatClientAgentRunOptions()
-{
-    ChatOptions = new() { ResponseFormat = responseFormat }
-});
-
-// Consume the SO result as text since there's no .NET type to deserialize into
-Console.WriteLine(response.Text);
-```
-
-### 1.6 SO in streaming scenarios
-
-In this scenario, the SO response is produced incrementally in parts via streaming. The caller specifies the `ResponseFormat` and consumes the response chunks as they arrive.
-Deserialization is performed after all chunks have been received.
-
-```csharp
-AIAgent agent = chatClient.AsAIAgent(new ChatClientAgentOptions()
-{
-    Name = "HelpfulAssistant",
-    ChatOptions = new()
-    {
-        Instructions = "You are a helpful assistant.",
-        ResponseFormat = ChatResponseFormat.ForJsonSchema<PersonInfo>()
-    }
-});
-
-IAsyncEnumerable<AgentResponseUpdate> updates = agent.RunStreamingAsync("Please provide information about John Smith, who is a 35-year-old software engineer.");
-
-AgentResponse response = await updates.ToAgentResponseAsync();
-
-// Deserialize the complete SO result after streaming is finished
-PersonInfo personInfo = JsonSerializer.Deserialize<PersonInfo>(response.Text)!;
-```
-
-## 2. SO usage via `RunAsync<T>` generic method
-
-This approach provides a convenient way to work with structured output on a per-run basis when the target type is known at compile time and a typed instance of the result
-is required.
-
-### Decision Drivers
-
-1. Support arrays and primitives as SO types
-2. Support complex types as SO types
-3. Work with `AIAgent` decorators (e.g., `OpenTelemetryAgent`)
-4. Enable SO for all AI agents, regardless of whether they natively support it
-
-### Considered Options
-
-1. `RunAsync<T>` as an instance method of `AIAgent` class delegating to virtual `RunCoreAsync<T>`
-2. `RunAsync<T>` as an extension method using feature collection
-3. `RunAsync<T>` as a method of the new `ITypedAIAgent` interface
-4. `RunAsync<T>` as an instance method of `AIAgent` class working via the new `AgentRunOptions.ResponseFormat` property
-
-### 1. `RunAsync<T>` as an instance method of `AIAgent` class delegating to virtual `RunCoreAsync<T>`
-
-This option adds the `RunAsync<T>` method directly to the `AIAgent` base class.
-
-```csharp
-public abstract class AIAgent
-{
-	public Task<AgentResponse<T>> RunAsync<T>(
-        IEnumerable<ChatMessage> messages,
-        AgentSession? session = null,
-        JsonSerializerOptions? serializerOptions = null,
-        AgentRunOptions? options = null,
-        CancellationToken cancellationToken = default)
-		=> this.RunCoreAsync<T>(messages, session, serializerOptions, options, cancellationToken);
-
-    protected virtual Task<AgentResponse<T>> RunCoreAsync<T>(
-        IEnumerable<ChatMessage> messages,
-        AgentSession? session = null,
-        JsonSerializerOptions? serializerOptions = null,
-        AgentRunOptions? options = null,
-        CancellationToken cancellationToken = default)
-    {
-        throw new NotSupportedException($"The agent of type '{this.GetType().FullName}' does not support typed responses.");
-    }
-}
-```
-
-Agents with native SO support override the `RunCoreAsync<T>` method to provide their implementation. If not overridden, the method throws a `NotSupportedException`.
-
-Users will call the generic `RunAsync<T>` method directly on the agent:
-
-```csharp
-AIAgent agent = chatClient.AsAIAgent(name: "HelpfulAssistant", instructions: "You are a helpful assistant.");
-
-AgentResponse<PersonInfo> response = await agent.RunAsync<PersonInfo>("Please provide information about John Smith, who is a 35-year-old software engineer.");
-```
-
-Decision drivers satisfied:
-1. Support arrays and primitives as SO types
-2. Support complex types as SO types
-3. Work with `AIAgent` decorators (e.g., `OpenTelemetryAgent`)
-4. Enable SO for all AI agents, regardless of whether they natively support it
-
-Pros:
- The `AIAgent.RunAsync<T>` method is easily discoverable.
- Both the SO decorator and `ChatClientAgent` have compile-time access to the type `T`, allowing them to use the native `IChatClient.GetResponseAsync<T>` API, which handles primitives and collections seamlessly.
-
-Cons:
- Agents without native SO support will still expose `RunAsync<T>`, which may be misleading.
- `ChatClientAgent` exposing `RunAsync<T>` may be misleading when the underlying chat client does not support SO.
- All `AIAgent` decorators must override `RunCoreAsync<T>` to properly handle `RunAsync<T>` calls.
-
-### 2. `RunAsync<T>` as an extension method using feature collection
-
-This option uses the Agent Framework feature collection (implemented via `AgentRunOptions.AdditionalProperties`) to pass a `StructuredOutputFeature` to agents, signaling that SO is requested.
-
-Agents with native SO support check for this feature. If present, they read the target type, build the schema, invoke the underlying API, and store the response back in the feature.
-```csharp
-public class StructuredOutputFeature
-{
-    public StructuredOutputFeature(Type outputType)
-    {
-        this.OutputType = outputType;
-    }
-
-    [JsonIgnore]
-    public Type OutputType { get; set; }
-
-    public JsonSerializerOptions? SerializerOptions { get; set; }
-
-    public AgentResponse? Response { get; set; }
-}
-```
-
-The `RunAsync<T>` extension method for `AIAgent` adds this feature to the collection.
-```csharp
-public static async Task<AgentResponse<T>> RunAsync<T>(
-    this AIAgent agent,
-    IEnumerable<ChatMessage> messages,
-    AgentSession? session = null,
-    JsonSerializerOptions? serializerOptions = null,
-    AgentRunOptions? options = null,
-    CancellationToken cancellationToken = default)
-{
-    // Create the structured output feature.
-    StructuredOutputFeature structuredOutputFeature = new(typeof(T))
-    {
-        SerializerOptions = serializerOptions,
-    };
-
-    // Register it in the feature collection.
-    ((options ??= new AgentRunOptions()).AdditionalProperties ??= []).Add(typeof(StructuredOutputFeature).FullName!, structuredOutputFeature);
-
-    var response = await agent.RunAsync(messages, session, options, cancellationToken).ConfigureAwait(false);
-
-    if (structuredOutputFeature.Response is not null)
-    {
-        return new StructuredOutputResponse<T>(structuredOutputFeature.Response, response, serializerOptions);
-    }
-
-    throw new InvalidOperationException("No structured output response was generated by the agent.");
-}
-```
-
-Users will call the `RunAsync<T>` extension method directly on the agent:
-
-```csharp
-AIAgent agent = chatClient.AsAIAgent(name: "HelpfulAssistant", instructions: "You are a helpful assistant.");
-
-AgentResponse<PersonInfo> response = await agent.RunAsync<PersonInfo>("Please provide information about John Smith, who is a 35-year-old software engineer.");
-```
-
-Decision drivers satisfied:
-1. Support arrays and primitives as SO types
-2. Support complex types as SO types
-3. Work with `AIAgent` decorators (e.g., `OpenTelemetryAgent`)
-4. Enable SO for all AI agents, regardless of whether they natively support it
-
-Pros:
- The `RunAsync<T>` extension method is easily discoverable.
- The `AIAgent` public API surface remains unchanged.
- No changes required to `AIAgent` decorators.
-
-Cons:
- Agents without native SO support will still expose `RunAsync<T>`, which may be misleading.
- `ChatClientAgent` exposing `RunAsync<T>` may be misleading when the underlying chat client does not support SO.
-
-### 3. `RunAsync<T>` as a method of the new `ITypedAIAgent` interface
-
-This option defines a new `ITypedAIAgent` interface that agents with SO support implement. Agents without SO support do not implement it, allowing users to check for SO capability via interface detection.
-
-The interface:
-```csharp
-public interface ITypedAIAgent
-{
-    Task<AgentResponse<T>> RunAsync<T>(
-        IEnumerable<ChatMessage> messages,
-        AgentSession? session = null,
-        JsonSerializerOptions? serializerOptions = null,
-        AgentRunOptions? options = null,
-        CancellationToken cancellationToken = default);
-
-    ...
-}
-```
-
-Agents with SO support implement this interface:
-```csharp
-public sealed partial class ChatClientAgent : AIAgent, ITypedAIAgent
-{
-    public async Task<AgentResponse<T>> RunAsync<T>(
-        IEnumerable<ChatMessage> messages,
-        AgentSession? session = null,
-        JsonSerializerOptions? serializerOptions = null,
-        AgentRunOptions? options = null,
-        CancellationToken cancellationToken = default)
-    {
-        ...
-    }
-}
-```
-
-However, `ChatClientAgent` presents a challenge: it can work with chat clients that either support or do not support SO. Implementing the interface does not guarantee 
-the underlying chat client supports SO, which undermines the core idea of using interface detection to determine SO capability.
-
-Additionally, to allow users to access interface methods on decorated agents, all decorators must implement `ITypedAIAgent`. This makes it difficult for users to 
-determine whether the underlying agent actually supports SO, further weakening the purpose of this approach.
-
-Furthermore, users would have to probe the agent type to check if it implements the `ITypedAIAgent` interface and cast it accordingly to access the `RunAsync<T>` methods.
-This adds friction to the user experience. A `RunAsync<T>` extension method for `AIAgent` could be provided to alleviate that.
-
-Given these drawbacks, this option is more complex to implement than the others without providing clear benefits.
-
-Decision drivers satisfied:
-1. Support arrays and primitives as SO types
-2. Support complex types as SO types
-3. Work with `AIAgent` decorators (e.g., `OpenTelemetryAgent`)
-4. Enable SO for all AI agents, regardless of whether they natively support it
-
-Pros:
- Both the SO decorator and `ChatClientAgent` have compile-time access to the type `T`, allowing them to use the native `IChatClient.GetResponseAsync<T>` API, which handles primitives and collections seamlessly.
-
-Cons:
- `ChatClientAgent` implementing `ITypedAIAgent` may be misleading when the underlying chat client does not support SO.
- All `AIAgent` decorators must implement `ITypedAIAgent` to handle `RunAsync<T>` calls.
- Decorators implementing the interface may mislead users into thinking the underlying agent natively supports SO.
- Agents must implement all members of `ITypedAIAgent`, not just a core method.
- Users must check the agent type and cast to `ITypedAIAgent` to access `RunAsync<T>`.
-
-### 4. `RunAsync<T>` as an instance method of `AIAgent` class working via the new `AgentRunOptions.ResponseFormat` property
-
-This option adds a `ResponseFormat` property of type `ChatResponseFormat` to `AgentRunOptions`. Agents that support SO check for the presence of 
-this property in the options passed to `RunAsync` to determine whether structured output is requested. If present, they use the schema from `ResponseFormat` 
-to invoke the underlying API and obtain the SO response.
-
-```csharp
-public class AgentRunOptions
-{
-    public ChatResponseFormat? ResponseFormat { get; set; }
-}
-```
-
-Additionally, a generic `RunAsync<T>` method is added to `AIAgent` that initializes the `ResponseFormat` based on the type `T` and delegates to the non-generic `RunAsync`.
-
-```csharp
-public abstract class AIAgent
-{
-	public async Task<AgentResponse<T>> RunAsync<T>(
-        IEnumerable<ChatMessage> messages,
-        AgentSession? session = null,
-        JsonSerializerOptions? serializerOptions = null,
-        AgentRunOptions? options = null,
-        CancellationToken cancellationToken = default)
-    {
-        serializerOptions ??= AgentAbstractionsJsonUtilities.DefaultOptions;
-
-        var responseFormat = ChatResponseFormat.ForJsonSchema<T>(serializerOptions);
-
-        options = options?.Clone() ?? new AgentRunOptions();
-        options.ResponseFormat = responseFormat;
-
-        AgentResponse response = await this.RunAsync(messages, session, options, cancellationToken).ConfigureAwait(false);
-
-        return new AgentResponse<T>(response, serializerOptions);
-    }
-}
-```
-
-Users call the generic `RunAsync<T>` method directly on the agent:
-
-```csharp
-AIAgent agent = chatClient.AsAIAgent(name: "HelpfulAssistant", instructions: "You are a helpful assistant.");
-
-AgentResponse<PersonInfo> response = await agent.RunAsync<PersonInfo>("Please provide information about John Smith, who is a 35-year-old software engineer.");
-```
-
-Decision drivers satisfied:
-1. Support arrays and primitives as SO types
-2. Support complex types as SO types
-3. Work with `AIAgent` decorators (e.g., `OpenTelemetryAgent`)
-4. Enable SO for all AI agents, regardless of whether they natively support it
-
-Pros:
- The `AIAgent.RunAsync<T>` method is easily discoverable.
- No changes required to `AIAgent` decorators
-
-Cons:
- Agents without native SO support will still expose `RunAsync<T>`, which may be misleading.
- `ChatClientAgent` exposing `RunAsync<T>` may be misleading when the underlying chat client does not support SO.
-
-### Decision Table
-
-|  | Option 1: Instance method + RunCoreAsync<T> | Option 2: Extension method + feature collection | Option 3: ITypedAIAgent Interface | Option 4: Instance method + AgentRunOptions.ResponseFormat |
-|---|---|---|---|---|
-| Discoverability | ✅ `RunAsync<T>` easily discoverable | ✅ `RunAsync<T>` easily discoverable | ❌ Requires type check and cast | ✅ `RunAsync<T>` easily discoverable |
-| Decorator changes | ❌ All decorators must override `RunCoreAsync<T>` | ✅ No changes required | ❌ All decorators must implement `ITypedAIAgent` | ✅ No changes required to decorators |
-| Primitives/collections handling | ✅ Native support via `IChatClient.GetResponseAsync<T>` | ❌ Must wrap/unwrap internally | ✅ Native support via `IChatClient.GetResponseAsync<T>` | ❌ Must wrap/unwrap internally |
-| Misleading API exposure | ❌ Agents without SO still expose `RunAsync<T>` | ❌ Agents without SO still expose `RunAsync<T>` | ❌ Interface on `ChatClientAgent` may be misleading | ❌ Agents without SO still expose `RunAsync<T>` |
-| Implementation burden | ❌ Decorators must override method | ❌ Must handle schema wrapping | ❌ Agents must implement all interface members | ✅ Delegates to existing `RunAsync` via `ResponseFormat` |
-
-## Cross-Cutting Aspects
-
-1. **The `useJsonSchemaResponseFormat` parameter**: The `ChatClientAgent.RunAsync<T>` method has this parameter to enable structured output on LLMs that do not natively support it.
-  It works by adding a user message like "Respond with a JSON value conforming to the following schema:" along with the JSON schema. However, this approach has not been reliable historically. The recommendation is not to carry this parameter forward, regardless of which option is chosen.
-
-2. **Primitives and array types handling**: There are a few options for how primitive and array types can be handled in the Agent Framework:
-
-   1. **Never wrap**, regardless of whether the schema is provided via `ResponseFormat` or `RunAsync<T>`.
-       - Pro: No changes needed; user has full control.
-       - Pro: No issues with unwrapping in streaming scenarios.
-       - Con: User must wrap manually.
-
-   2. **Always wrap**, regardless of whether the schema is provided via `ResponseFormat` or `RunAsync<T>`.
-       - Pro: Consistent wrapping behavior; no manual wrapping needed.
-       - Con: Inconsistent unwrapping behavior; it may be unexpected to have SO result wrapped when schema is provided via `ResponseFormat`.
-       - Con: Impossible to know if SO result is wrapped to unwrap it in streaming scenarios.
-
-   3. **Wrap only for `RunAsync<T>`** and do not wrap the schema provided via `ResponseFormat`.
-       - Pro: No unexpectedly wrapped result when schema is provided via `ResponseFormat`.
-       - Pro: Solves the problem with unwrapping in streaming scenarios.
-
-   4. **User decides** whether to wrap schema provided via `ResponseFormat` using a new `wrapPrimitivesAndArrays` property of `ChatResponseFormatJson`. For SO provided via `RunAsync<T>`, AF always wraps.
-       - Pro: No manual wrapping needed; just flip a switch.
-       - Pro: Solves the problem with unwrapping in streaming scenarios.
-       - Con: Extends the public API surface.
-
-3. **Structured output for agents without native SO support**: Some AI agents in AF do not support structured output natively. This is either because it is not part of the protocol (e.g., A2A agent) or because the agents use LLMs without structured output capabilities.
-   To address this gap, AF can provide the `StructuredOutputAgent` decorator. This decorator wraps any `AIAgent` and adds structured output support by obtaining the text response from the decorated agent and delegating it to a configured chat client for JSON transformation.
-   
-   ```csharp
-   public class StructuredOutputAgent : DelegatingAIAgent
-   {
-        private readonly IChatClient _chatClient;
-
-        public StructuredOutputAgent(AIAgent innerAgent, IChatClient chatClient)
-            : base(innerAgent)
-        {
-            this._chatClient = Throw.IfNull(chatClient);
-        }
-
-        protected override async Task<AgentResponse<T>> RunCoreAsync<T>(
-            IEnumerable<ChatMessage> messages,
-            AgentSession? session = null,
-            JsonSerializerOptions? serializerOptions = null,
-            AgentRunOptions? options = null,
-            CancellationToken cancellationToken = default)
-        {
-            // Run the inner agent first, to get back the text response we want to convert.
-            var textResponse = await this.InnerAgent.RunAsync(messages, session, options, cancellationToken).ConfigureAwait(false);
-
-            // Invoke the chat client to transform the text output into structured data.
-            ChatResponse<T> soResponse = await this._chatClient.GetResponseAsync<T>(
-                messages:
-                [
-                    new ChatMessage(ChatRole.System, "You are a json expert and when provided with any text, will convert it to the requested json format."),
-                    new ChatMessage(ChatRole.User, textResponse.Text)
-                ],
-                serializerOptions: serializerOptions ?? AgentJsonUtilities.DefaultOptions,
-                cancellationToken: cancellationToken).ConfigureAwait(false);
-
-            return new StructuredOutputAgentResponse(soResponse, textResponse);
-        }
-   }
-   ```
-
-   The decorator preserves the original response from the decorated agent and surfaces it via the `OriginalResponse` property on the returned `StructuredOutputAgentResponse`.
-   This allows users to access both the original unstructured response and the new structured response when using this decorator.
-   ```csharp
-   public class StructuredOutputAgentResponse : AgentResponse
-   {
-       internal StructuredOutputAgentResponse(ChatResponse chatResponse, AgentResponse agentResponse) : base(chatResponse)
-       {
-           this.OriginalResponse = agentResponse;
-       }
-       
-       public AgentResponse OriginalResponse { get; }
-    }
-   ```
-   
-   The decorator can be registered during the agent configuration step using the `UseStructuredOutput` extension method on `AIAgentBuilder`.
-
-   ```csharp
-   IChatClient meaiChatClient = chatClient.AsIChatClient();
-
-   AIAgent baseAgent = meaiChatClient.AsAIAgent(name: "HelpfulAssistant", instructions: "You are a helpful assistant.");
-
-   // Register the StructuredOutputAgent decorator during agent building
-   AIAgent agent = baseAgent
-       .AsBuilder()
-       .UseStructuredOutput(meaiChatClient)
-       .Build();
-
-   AgentResponse<PersonInfo> response = await agent.RunAsync<PersonInfo>("Please provide information about John Smith, who is a 35-year-old software engineer.");
-
-   Console.WriteLine($"Name: {response.Result.Name}");
-   Console.WriteLine($"Age: {response.Result.Age}");
-   Console.WriteLine($"Occupation: {response.Result.Occupation}");
-   
-   var originalResponse = ((StructuredOutputAgentResponse)response.RawRepresentation!).OriginalResponse;
-   Console.WriteLine($"Original unstructured response: {originalResponse.Text}");
-
-   ```
-
-## Decision Outcome
-
-It was decided to keep both approaches for structured output - via `ResponseFormat` and via `RunAsync<T>` since they serve different scenarios and use cases.
-
-For the `RunAsync<T>` approach, option 4 was selected, which adds a generic `RunAsync<T>` method to `AIAgent` that works via the new `AgentRunOptions.ResponseFormat` property.
-This was chosen for its simplicity and because no changes are required to existing `AIAgent` decorators.
-
-For cross-cutting aspects, the `useJsonSchemaResponseFormat` parameter will not be carried forward due to reliability issues.
-
-For handling primitives and array types, option 3 was selected: wrap only for `RunAsync<T>` and do not wrap the schema provided via `ResponseFormat`.
-This avoids the issues described in the Approach 1 section note.
-
-Finally, it was decided not to include the `StructuredOutputAgent` decorator in the framework, since the reliability of producing structured output via an additional
-LLM call may not be sufficient for all scenarios. Instead, this pattern is provided as a sample to demonstrate how structured output can be achieved for agents without native support,
-giving users a reference implementation they can adapt to their own requirements.
@@ -1,48 +0,0 @@
-# AGENTS.md
-
-Instructions for AI coding agents working on durable agents documentation.
-
-## Scope
-
-This directory contains feature documentation for the durable agents integration. The source code and samples live elsewhere:
-
- .NET implementation: `dotnet/src/Microsoft.Agents.AI.DurableTask/` and `dotnet/src/Microsoft.Agents.AI.Hosting.AzureFunctions/`
- Python implementation: `python/packages/durabletask/` and `python/packages/azurefunctions/` (package `agent-framework-azurefunctions`)
- .NET samples: `dotnet/samples/Durable/Agents/`
- Python samples: `python/samples/04-hosting/durabletask/`
- Official docs (Microsoft Learn): <https://learn.microsoft.com/agent-framework/integrations/azure-functions>
-
-## Document structure
-
-| File | Purpose |
-| --- | --- |
-| `README.md` | Main technical overview: architecture, hosting models, orchestration patterns, and links to samples. |
-| `durable-agents-ttl.md` | Deep-dive on session Time-To-Live (TTL) configuration and behavior. |
-
-Add new sibling documents when a topic is too detailed for the README (e.g., a new feature like reliable streaming or MCP tool exposure). Keep the README focused on orientation and link out to siblings for depth.
-
-## Writing guidelines
-
- **Audience**: Developers already familiar with the Microsoft Agent Framework who want to understand what durability adds and how to use it.
- **Host-agnostic first**: Durable agents work in console apps, Azure Functions, and any Durable Task–compatible host. Show host-agnostic patterns (plain orchestration functions, `IServiceCollection` registration) before Azure Functions–specific patterns. Avoid giving the impression that Azure Functions is the only hosting option.
- **Both languages**: Always include C# and Python examples side by side. Keep them equivalent in functionality.
- **Callout syntax**: Use GitHub-flavored callouts (`> [!NOTE]`, `> [!IMPORTANT]`, `> [!WARNING]`) rather than bold-text callouts (`> **Note:** ...`).
- **Line length**: Do not wrap long lines. Rely on text viewers / renderers for line wrapping.
- **Tables**: Use spaces around pipes in separator rows (`| --- |` not `|---|`).
- **Code snippets**: Keep them minimal and self-contained. Omit boilerplate (using statements, environment variable reads) unless the snippet is specifically about setup.
- **Cross-references**: Link to Microsoft Learn for conceptual background (Durable Entities, Durable Task Scheduler, Azure Functions). Link to sibling docs within this directory for feature deep-dives.
-
-## Linting
-
-Run markdownlint on all documents before committing, with line-length checks disabled:
-
-```bash
-markdownlint docs/features/durable-agents/ --disable MD013
-```
-
-## When to update these docs
-
- A new durable agent feature is added (e.g., a new orchestration pattern, hosting model, or configuration option).
- The public API surface changes in a way that affects how developers use durable agents.
- New sample directories are added — update the sample links in README.md.
- The official Microsoft Learn documentation is restructured — update external links.
@@ -1,239 +0,0 @@
-# Durable agents
-
-## Overview
-
-Durable agents extend the standard Microsoft Agent Framework with **durable state management** powered by the Durable Task framework. An ordinary Agent Framework agent runs in-process: its conversation history lives in memory and is lost when the process ends. A durable agent persists conversation history and execution state in external storage so that sessions survive process restarts, failures, and scale-out events.
-
-| Capability | Ordinary agent | Durable agent |
-| --- | --- | --- |
-| Conversation history | In-memory only | Durably persisted |
-| Failure recovery | State lost on crash | Automatically resumed |
-| Multi-instance scale-out | Not supported | Any worker can resume a session |
-| Multi-agent orchestrations | Manual coordination | Deterministic, checkpointed workflows |
-| Human-in-the-loop | Must keep process alive | Can wait days/weeks with zero compute |
-| Hosting | Any process | Console app, Azure Functions, or any Durable Task–compatible host |
-
-> [!NOTE]
-> For a step-by-step tutorial and deployment guidance, see [Azure Functions (Durable)](https://learn.microsoft.com/agent-framework/integrations/azure-functions) on Microsoft Learn.
-
-## How durable agents work
-
-Durable agents are implemented on top of [Durable Entities](https://learn.microsoft.com/azure/azure-functions/durable/durable-functions-entities) (also called "virtual actors"). Each **agent session** maps to one entity instance whose state contains the full conversation history. When you send a message to a durable agent, the following happens:
-
-1. The message is dispatched to the entity identified by an `AgentSessionId` (a composite of the agent name and a unique session key).
-2. The entity loads its persisted `DurableAgentState`, which includes the complete conversation history.
-3. The entity invokes the underlying `AIAgent` with the full conversation history, collects the response, and appends both the request and the response to the state.
-4. The updated state is persisted back to durable storage automatically.
-
-Because the entity framework serializes access to each entity instance, concurrent messages to the same session are processed one at a time, eliminating race conditions.
-
-### Agent session identity
-
-Every durable agent session is identified by an `AgentSessionId`, which has two components:
-
- **Name** – the registered name of the agent (case-insensitive).
- **Key** – a unique session key (case-sensitive), typically a GUID.
-
-The session ID is mapped to an underlying Durable Task entity ID with a `dafx-` prefix (e.g., `dafx-joker`). This naming convention is consistent across both .NET and Python implementations.
-
-## Architecture
-
-### .NET
-
-The .NET implementation consists of two NuGet packages:
-
-| Package | Purpose |
-| --- | --- |
-| `Microsoft.Agents.AI.DurableTask` | Core durable agent types: `DurableAIAgent`, `AgentEntity`, `DurableAgentSession`, `AgentSessionId`, `DurableAgentsOptions`, and the state model. |
-| `Microsoft.Agents.AI.Hosting.AzureFunctions` | Azure Functions hosting integration: auto-generated HTTP endpoints, MCP tool triggers, entity function triggers, and the `ConfigureDurableAgents` extension method on `FunctionsApplicationBuilder`. |
-
-Key types:
-
- **`DurableAIAgent`** – A subclass of `AIAgent` used *inside orchestrations*. Obtained via `context.GetAgent("agentName")`, it routes `RunAsync` calls through the orchestration's entity APIs so that each call is checkpointed.
- **`DurableAIAgentProxy`** – A subclass of `AIAgent` used *outside orchestrations* (e.g., from HTTP triggers or console apps). It signals the entity via `DurableTaskClient` and polls for the response.
- **`AgentEntity`** – The `TaskEntity<DurableAgentState>` that hosts the real agent. It loads the registered `AIAgent` by name, wraps it in an `EntityAgentWrapper`, feeds it the full conversation history, and persists the result.
- **`DurableAgentSession`** – An `AgentSession` subclass that carries the `AgentSessionId`.
- **`DurableAgentsOptions`** – Builder for registering agents and configuring TTL.
-
-### Python
-
-The core Python implementation is in the `agent-framework-durabletask` package (`python/packages/durabletask`). Azure Functions hosting (including `AgentFunctionApp`) is in the separate `agent-framework-azurefunctions` package (`python/packages/azurefunctions`).
-
-Key types:
-
- **`DurableAIAgent`** – A generic proxy (`DurableAIAgent[TaskT]`) implementing `SupportsAgentRun`. Returns a `TaskT` from `run()` — either an `AgentResponse` (client context) or a `DurableAgentTask` (orchestration context, must be `yield`ed).
- **`DurableAIAgentWorker`** – Wraps a `TaskHubGrpcWorker` and registers agents as durable entities via `add_agent()`.
- **`DurableAIAgentClient`** – Wraps a `TaskHubGrpcClient` for external callers. `get_agent()` returns a `DurableAIAgent[AgentResponse]`.
- **`DurableAIAgentOrchestrationContext`** – Wraps an `OrchestrationContext` for use inside orchestrations. `get_agent()` returns a `DurableAIAgent[DurableAgentTask]`.
- **`AgentEntity`** – Platform-agnostic agent execution logic that manages state, invokes the agent, handles streaming, and calls response callbacks.
-
-## Hosting models
-
-### Azure Functions
-
-The recommended production hosting model. A single call to `ConfigureDurableAgents` (C#) or `AgentFunctionApp` (Python) automatically:
-
- Registers agent entities with the Durable Task worker.
- Generates HTTP endpoints at `/api/agents/{agentName}/run` for each registered agent.
- Supports `thread_id` query parameter / JSON field and the `x-ms-thread-id` response header for session continuity.
- Supports fire-and-forget via the `x-ms-wait-for-response: false` header (returns HTTP 202).
- Optionally exposes agents as MCP tools.
-
-**C# example:**
-
-```csharp
-using IHost app = FunctionsApplication
-    .CreateBuilder(args)
-    .ConfigureFunctionsWebApplication()
-    .ConfigureDurableAgents(options => options.AddAIAgent(agent))
-    .Build();
-app.Run();
-```
-
-**Python example:**
-
-```python
-app = AgentFunctionApp(agents=[agent])
-```
-
-### Console apps / generic hosts
-
-For self-hosted or non-serverless scenarios, register durable agents via `IServiceCollection.ConfigureDurableAgents` (.NET) or `DurableAIAgentWorker` (Python) with explicit Durable Task worker and client configuration.
-
-**C# example:**
-
-```csharp
-IHost host = Host.CreateDefaultBuilder(args)
-    .ConfigureServices(services =>
-    {
-        services.ConfigureDurableAgents(
-            options => options.AddAIAgent(agent),
-            workerBuilder: b => b.UseDurableTaskScheduler(connectionString),
-            clientBuilder: b => b.UseDurableTaskScheduler(connectionString));
-    })
-    .Build();
-```
-
-**Python example:**
-
-```python
-worker = DurableAIAgentWorker(TaskHubGrpcWorker(host_address="localhost:4001"))
-worker.add_agent(agent)
-worker.start()
-```
-
-## Deterministic multi-agent orchestrations
-
-Durable agents can be composed into deterministic, checkpointed workflows using Durable Task orchestrations. The orchestration framework replays orchestrator code on failure, so completed agent calls are not re-executed.
-
-### Patterns
-
-| Pattern | Description |
-| --- | --- |
-| **Sequential (chaining)** | Call agents one after another, passing outputs forward. |
-| **Parallel (fan-out/fan-in)** | Run multiple agents concurrently and aggregate results. |
-| **Conditional** | Branch orchestration logic based on structured agent output. |
-| **Human-in-the-loop** | Pause for external events (approvals, feedback) with optional timeouts. |
-
-### Using agents in orchestrations
-
-Inside an orchestration function, obtain a `DurableAIAgent` via the orchestration context. Each agent gets its own session (created with `CreateSessionAsync` / `create_session`), and you can call the same agent multiple times on the same session to maintain conversation context across sequential invocations.
-
-**C#:**
-
-```csharp
-static async Task<string> WritingOrchestration(TaskOrchestrationContext context)
-{
-    // Get a durable agent reference — works in any host (console app, Azure Functions, etc.)
-    DurableAIAgent writer = context.GetAgent("WriterAgent");
-
-    // Create a session to maintain conversation context across multiple calls
-    AgentSession session = await writer.CreateSessionAsync();
-
-    // First call: generate an initial draft
-    AgentResponse<TextResponse> draft = await writer.RunAsync<TextResponse>(
-        message: "Write a concise inspirational sentence about learning.",
-        session: session);
-
-    // Second call: refine the draft — the agent sees the full conversation history
-    AgentResponse<TextResponse> refined = await writer.RunAsync<TextResponse>(
-        message: $"Improve this further while keeping it under 25 words: {draft.Result.Text}",
-        session: session);
-
-    return refined.Result.Text;
-}
-```
-
-**Python:**
-
-```python
-def writing_orchestration(context, _):
-    agent_ctx = DurableAIAgentOrchestrationContext(context)
-
-    # Get a durable agent reference — works in any host (standalone worker, Azure Functions, etc.)
-    writer = agent_ctx.get_agent("WriterAgent")
-
-    # Create a session to maintain conversation context across multiple calls
-    session = writer.create_session()
-
-    # First call: generate an initial draft
-    draft = yield writer.run(
-        messages="Write a concise inspirational sentence about learning.",
-        session=session,
-    )
-
-    # Second call: refine the draft — the agent sees the full conversation history
-    refined = yield writer.run(
-        messages=f"Improve this further while keeping it under 25 words: {draft.text}",
-        session=session,
-    )
-
-    return refined.text
-```
-
-> [!IMPORTANT]
-> In .NET, `DurableAIAgent.RunAsync<T>` deliberately avoids `ConfigureAwait(false)` because the Durable Task Framework uses a custom synchronization context — all continuations must run on the orchestration thread.
-
-## Streaming and response callbacks
-
-Durable agents do not support true end-to-end streaming because entity operations are request/response. However, **reliable streaming** is supported via response callbacks:
-
- **`IAgentResponseHandler`** (.NET) or **`AgentResponseCallbackProtocol`** (Python) – Implement this interface to receive streaming updates as the underlying agent generates them (e.g., push tokens to a Redis Stream for client consumption).
- The entity still returns the complete `AgentResponse` after the stream is fully consumed.
- Clients can reconnect and resume reading from a cursor-based stream (e.g., Redis Streams) without losing messages.
-
-See the **Reliable Streaming** samples for a complete implementation using Redis Streams.
-
-## Session TTL (Time-To-Live)
-
-Durable agent sessions support automatic cleanup via configurable TTL. See [Session TTL](durable-agents-ttl.md) for details on configuration, behavior, and best practices.
-
-## Observability
-
-When using the [Durable Task Scheduler](https://learn.microsoft.com/azure/azure-functions/durable/durable-task-scheduler/durable-task-scheduler) as the durable backend, you get built-in observability through its dashboard:
-
- **Conversation history** – View complete chat history for each agent session.
- **Orchestration visualization** – See multi-agent execution flows, including parallel branches and conditional logic.
- **Performance metrics** – Monitor agent response times, token usage, and orchestration duration.
- **Debugging** – Trace tool invocations and external event handling.
-
-## Samples
-
- **.NET** – [Console app samples](../../../dotnet/samples/Durable/Agents/ConsoleApps/) and [Azure Functions samples](../../../dotnet/samples/Durable/Agents/AzureFunctions/) covering single-agent, chaining, concurrency, conditionals, human-in-the-loop, long-running tools, MCP tool exposure, and reliable streaming.
- **Python** – [Durable Task samples](../../../python/samples/04-hosting/durabletask/) covering single-agent, multi-agent, streaming, chaining, concurrency, conditionals, and human-in-the-loop.
-
-## Packages
-
-| Language | Package | Source |
-| --- | --- | --- |
-| .NET | `Microsoft.Agents.AI.DurableTask` | [`dotnet/src/Microsoft.Agents.AI.DurableTask`](../../../dotnet/src/Microsoft.Agents.AI.DurableTask) |
-| .NET | `Microsoft.Agents.AI.Hosting.AzureFunctions` | [`dotnet/src/Microsoft.Agents.AI.Hosting.AzureFunctions`](../../../dotnet/src/Microsoft.Agents.AI.Hosting.AzureFunctions) |
-| Python | `agent-framework-durabletask` | [`python/packages/durabletask`](../../../python/packages/durabletask) |
-| Python | `agent-framework-azurefunctions` | [`python/packages/azurefunctions`](../../../python/packages/azurefunctions) |
-
-## Further reading
-
- [Azure Functions (Durable) — Microsoft Learn](https://learn.microsoft.com/agent-framework/integrations/azure-functions)
- [Durable Task Scheduler](https://learn.microsoft.com/azure/azure-functions/durable/durable-task-scheduler/durable-task-scheduler)
- [Durable Entities](https://learn.microsoft.com/azure/azure-functions/durable/durable-functions-entities)
- [Session TTL](durable-agents-ttl.md)
@@ -1,85 +0,0 @@
---
-name: build-and-test
-description: How to build and test .NET projects in the Agent Framework repository. Use this when verifying or testing changes.
---
-
- Only **UnitTest** projects need to be run locally; IntegrationTests require external dependencies.
- See `../project-structure/SKILL.md` for project structure details.
-
-## Build, Test, and Lint Commands
-
-```bash
-# From dotnet/ directory
-dotnet restore --tl:off   # Restore dependencies for all projects
-dotnet build --tl:off     # Build all projects
-dotnet test               # Run all tests
-dotnet format             # Auto-fix formatting for all projects
-
-# Build/test/format a specific project (preferred for isolated/internal changes)
-dotnet build src/Microsoft.Agents.AI.<Package> --tl:off
-dotnet test tests/Microsoft.Agents.AI.<Package>.UnitTests
-dotnet format src/Microsoft.Agents.AI.<Package>
-
-# Run a single test
-dotnet test --filter "FullyQualifiedName~Namespace.TestClassName.TestMethodName"
-
-# Run unit tests only
-dotnet test --filter FullyQualifiedName\~UnitTests
-```
-
-Use `--tl:off` when building to avoid flickering when running commands in the agent.
-
-## Speeding Up Builds and Testing
-
-The full solution is large. Use these shortcuts:
-
-| Change type | What to do |
-|-------------|------------|
-| Isolated/Internal logic | Build only the affected project and its `*.UnitTests` project. Fix issues, then build the full solution and run all unit tests. |
-| Public API surface | Build the full solution and run all unit tests immediately. |
-
-Example: Building a single code project for all target frameworks
-
-```bash
-# From dotnet/ directory
-dotnet build ./src/Microsoft.Agents.AI.Abstractions
-```
-
-Example: Building a single code project for just .NET 10.
-
-```bash
-# From dotnet/ directory
-dotnet build ./src/Microsoft.Agents.AI.Abstractions -f net10.0
-```
-
-Example: Running tests for a single project using .NET 10.
-
-```bash
-# From dotnet/ directory
-dotnet test ./tests/Microsoft.Agents.AI.Abstractions.UnitTests -f net10.0
-```
-
-Example: Running a single test in a specific project using .NET 10.
-Provide the full namespace, class name, and method name for the test you want to run:
-
-```bash
-# From dotnet/ directory
-dotnet test ./tests/Microsoft.Agents.AI.Abstractions.UnitTests -f net10.0 --filter "FullyQualifiedName~Microsoft.Agents.AI.Abstractions.UnitTests.AgentRunOptionsTests.CloningConstructorCopiesProperties"
-```
-
-### Multi-target framework tip
-
-Most projects target multiple .NET frameworks. If the affected code does **not** use `#if` directives for framework-specific logic, pass `-f net10.0` to speed up building and testing.
-
-### Package Restore tip
-
-`dotnet build` will try and restore packages for all projects on each build, which can be slow.
-Unless packages have been changed, or it's the first time building the solution, add `--no-restore` to the build command to skip this step and speed up builds.
-
-Just remember to run `dotnet restore` after pulling changes, making changes to project references, or when building for the first time.
-
-### Testing on Linux tip
-
-Unit tests target both .NET Framework as well as .NET Core. When running on Linux, only the .NET Core tests can be run, as .NET Framework is not supported on Linux.
-
-To run only the .NET Core tests, use the `-f net10.0` option with `dotnet test`.
@@ -1,31 +0,0 @@
---
-name: project-structure
-description: Explains the project structure of the agent-framework .NET solution
---
-
-# Agent Framework .NET Project Structure
-
-```
-dotnet/
-├── src/
-│   ├── Microsoft.Agents.AI/                      # Core AI agent implementations
-│   ├── Microsoft.Agents.AI.Abstractions/         # Core AI agent abstractions
-│   ├── Microsoft.Agents.AI.A2A/                  # Agent-to-Agent (A2A) provider
-│   ├── Microsoft.Agents.AI.OpenAI/               # OpenAI provider
-│   ├── Microsoft.Agents.AI.AzureAI/              # Azure AI Foundry Agents (v2) provider
-│   ├── Microsoft.Agents.AI.AzureAI.Persistent/   # Legacy Azure AI Foundry Agents (v1) provider
-│   ├── Microsoft.Agents.AI.Anthropic/            # Anthropic provider
-│   ├── Microsoft.Agents.AI.Workflows/            # Workflow orchestration
-│   └── ...                                       # Other packages
-├── samples/                                      # Sample applications
-└── tests/                                        # Unit and integration tests
-```
-
-## Main Folders
-
-| Folder | Contents |
-|--------|----------|
-| `src/` | Source code projects |
-| `tests/` | Test projects — named `<Source-Code-Project>.UnitTests` or `<Source-Code-Project>.IntegrationTests` |
-| `samples/` | Sample projects |
-| `src/Shared`, `src/LegacySupport` | Shared code files included by multiple source code projects (see README.md files in these folders or their subdirectories for instructions on how to include them in a project) |
@@ -1,82 +0,0 @@
---
-name: verify-dotnet-samples
-description: How to build, run and verify the .NET sample projects in the Agent Framework repository. Use this when a user wants to verify that the samples still function as expected.
---
-
-# Verifying .NET Sample Projects
-
-## Sample Pre-requisites
-
-We should only support verifying samples that:
-1. Use environment variables for configuration.
-2. Have no complex setup requirements, e.g., where multiple applications need to be run together, or where we need to launch a browser, etc.
-
-Always report to the user which samples were run and which were not, and why.
-
-## Verifying a sample
-
-Samples should be verified to ensure that they actually work as intended and that their output matches what is expected.
-For each sample that is run, output should be produced that shows the result and explains the reasoning about what output
-was expected, what was produced, and why it didn't match what the sample was expected to produce.
-
-Steps to verify a sample:
-1. Read the code for the sample
-1. Check what environment variables are required for the sample
-1. Check if each environment variable has been set
-1. If there are any missing, give the user a list of missing environment variables to set and terminate
-1. Summarize what the expected output of the sample should be
-1. Run the sample
-1. Show the user any output from the sample run as it gets produced, so that they can see the run progress
-1. Check the output of the run against expectations
-1. After running all requested samples, produce output for each sample that was verified:
-  1. If expectations were matched, output the following:
-     ```text
-     [Sample Name] Succeeded
-     ```
-  1. If expectations were not matched, output the following:
-     ```text
-     [Sample Name] Failed
-     Actual Output:
-     [What the sample produced]
-     Expected Output:
-     [Explanation of what was expected and why the actual output didn't match expectations]
-     ```
-
-## Environment Variables
-
-Most samples use environment variables to configure settings.
-
-```csharp
-var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
-var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
-```
-
-To run a sample, the environment variables should be set first.
-Before running a sample, check whether each environment variable in the sample has a value and
-then give the user a list of environment variables to set.
-
-You can provide the user some examples of how to set the variables like this:
-
-```bash
-export AZURE_OPENAI_ENDPOINT="https://my-openai-instance.openai.azure.com/"
-export AZURE_OPENAI_DEPLOYMENT_NAME="gpt-4o-mini"
-```
-
-To check if a variable has a value use e.g.:
-
-```bash
-echo $AZURE_OPENAI_ENDPOINT
-```
-
-## How to Run a Sample (General Pattern)
-
-```bash
-cd dotnet/samples/<category>/<sample-dir>
-dotnet run
-```
-
-For multi-targeted projects (e.g., Durable console apps), specify the framework:
-
-```bash
-dotnet run --framework net10.0
-```
@@ -1,6 +1,5 @@
 {
    "dotnet.defaultSolution": "agent-framework-dotnet.slnx",
    "git.openRepositoryInParentFolders": "always",
-    "chat.agent.enabled": true,
-    "dotnet.automaticallySyncWithActiveItem": true
+    "chat.agent.enabled": true
 }
@@ -1,66 +0,0 @@
-# AGENTS.md
-
-Instructions for AI coding agents working in the .NET codebase.
-
-## Build, Test, and Lint Commands
-
-See `./.github/skills/build-and-test/SKILL.md` for detailed instructions on building, testing, and linting projects.
-
-## Project Structure
-
-See `./.github/skills/project-structure/SKILL.md` for an overview of the project structure.
-
-### Core types
-
- `AIAgent`: The abstract base class that all agents derive from, providing common methods for interacting with an agent.
- `AgentSession`: The abstract base class that all agent sessions derive from, representing a conversation with an agent.
- `ChatClientAgent`: An `AIAgent` implementation that uses an `IChatClient` to send messages to an AI provider and receive responses.
- `IChatClient`: Interface for sending messages to an AI provider and receiving responses. Used by `ChatClientAgent` and implemented by provider-specific packages.
- `FunctionInvokingChatClient`: Decorator for `IChatClient` that adds function invocation capabilities.
- `AITool`: Represents a tool that an agent/AI provider can use, with metadata and an execution delegate.
- `AIFunction`: A specific type of `AITool` that represents a local function the agent/AI provider can call, with parameters and return types defined.
- `ChatMessage`: Represents a message in a conversation.
- `AIContent`: Represents content in a message, which can be text, a function call, tool output and more.
-
-### External Dependencies
-
-The framework integrates with `Microsoft.Extensions.AI` and `Microsoft.Extensions.AI.Abstractions` (external NuGet packages)
-using types like `IChatClient`, `FunctionInvokingChatClient`, `AITool`, `AIFunction`, `ChatMessage`, and `AIContent`.
-
-## Key Conventions
-
- **Encoding**: All new files must be saved with UTF-8 encoding with BOM (Byte Order Mark). This is required for `dotnet format` to work correctly.
- **Copyright header**: `// Copyright (c) Microsoft. All rights reserved.` at top of all `.cs` files
- **XML docs**: Required for all public methods and classes
- **Async**: Use `Async` suffix for methods returning `Task`/`ValueTask`
- **Private classes**: Should be `sealed` unless subclassed
- **Config**: Read from environment variables with `UPPER_SNAKE_CASE` naming
- **Tests**: Add Arrange/Act/Assert comments; use Moq for mocking
-
-## Key Design Principles
-
-When developing or reviewing code, verify adherence to these key design principles:
-
- **DRY**: Avoid code duplication by moving common logic into helper methods or helper classes.
- **Single Responsibility**: Each class should have one clear responsibility.
- **Encapsulation**: Keep implementation details private and expose only necessary public APIs.
- **Strong Typing**: Use strong typing to ensure that code is self-documenting and to catch errors at compile time.
-
-## Sample Structure
-
-Samples (in `./samples/` folder) should follow this structure:
-
-1. Copyright header: `// Copyright (c) Microsoft. All rights reserved.`
-2. Description comment explaining what the sample demonstrates
-3. Using statements
-4. Main code logic
-5. Helper methods at bottom
-
-Configuration via environment variables (never hardcode secrets). Keep samples simple and focused.
-
-When adding a new sample:
-
- Create a standalone project in `samples/` with matching directory and project names
- Include a README.md explaining what the sample does and how to run it
- Add the project to the solution file
- Reference the sample in the parent directory's README.md
@@ -11,8 +11,8 @@
  </PropertyGroup>
  <ItemGroup>
    <!-- Aspire.* -->
-    <PackageVersion Include="Anthropic" Version="12.3.0" />
-    <PackageVersion Include="Anthropic.Foundry" Version="0.4.1" />
+    <PackageVersion Include="Anthropic" Version="12.0.1" />
+    <PackageVersion Include="Anthropic.Foundry" Version="0.1.0" />
    <PackageVersion Include="Aspire.Azure.AI.OpenAI" Version="13.0.0-preview.1.25560.3" />
    <PackageVersion Include="Aspire.Hosting.AppHost" Version="$(AspireAppHostSdkVersion)" />
    <PackageVersion Include="Aspire.Hosting.Azure.CognitiveServices" Version="$(AspireAppHostSdkVersion)" />
@@ -33,18 +33,18 @@
    <!-- Newtonsoft.Json -->
    <PackageVersion Include="Newtonsoft.Json" Version="13.0.4" />
    <!-- System.* -->
-    <PackageVersion Include="Microsoft.Bcl.AsyncInterfaces" Version="10.0.3" />
+    <PackageVersion Include="Microsoft.Bcl.AsyncInterfaces" Version="10.0.2" />
    <PackageVersion Include="Microsoft.Bcl.HashCode" Version="6.0.0" />
    <PackageVersion Include="System.ClientModel" Version="1.8.1" />
    <PackageVersion Include="System.CodeDom" Version="10.0.0" />
-    <PackageVersion Include="System.Collections.Immutable" Version="10.0.1" />
+    <PackageVersion Include="System.Collections.Immutable" Version="10.0.0" />
    <PackageVersion Include="System.CommandLine" Version="2.0.0-rc.2.25502.107" />
-    <PackageVersion Include="System.Diagnostics.DiagnosticSource" Version="10.0.3" />
+    <PackageVersion Include="System.Diagnostics.DiagnosticSource" Version="10.0.2" />
    <PackageVersion Include="System.Linq.AsyncEnumerable" Version="10.0.0" />
    <PackageVersion Include="System.Net.Http.Json" Version="10.0.0" />
-    <PackageVersion Include="System.Net.ServerSentEvents" Version="10.0.3" />
-    <PackageVersion Include="System.Text.Json" Version="10.0.3" />
-    <PackageVersion Include="System.Threading.Channels" Version="10.0.3" />
+    <PackageVersion Include="System.Net.ServerSentEvents" Version="10.0.0" />
+    <PackageVersion Include="System.Text.Json" Version="10.0.2" />
+    <PackageVersion Include="System.Threading.Channels" Version="10.0.2" />
    <PackageVersion Include="System.Threading.Tasks.Extensions" Version="4.6.3" />
    <PackageVersion Include="System.Net.Security" Version="4.3.2" />
    <!-- OpenTelemetry -->
@@ -61,12 +61,9 @@
    <PackageVersion Include="Microsoft.AspNetCore.OpenApi" Version="10.0.0" />
    <PackageVersion Include="Swashbuckle.AspNetCore.SwaggerUI" Version="10.0.0" />
    <!-- Microsoft.Extensions.* -->
-    <PackageVersion Include="Microsoft.Extensions.AI" Version="10.3.0" />
-    <PackageVersion Include="Microsoft.Extensions.AI.Abstractions" Version="10.3.0" />
-    <PackageVersion Include="Microsoft.Extensions.AI.Evaluation" Version="10.3.0" />
-    <PackageVersion Include="Microsoft.Extensions.AI.Evaluation.Quality" Version="10.3.0" />
-    <PackageVersion Include="Microsoft.Extensions.AI.Evaluation.Safety" Version="10.3.0-preview.1.26109.11" />
-    <PackageVersion Include="Microsoft.Extensions.AI.OpenAI" Version="10.3.0" />
+    <PackageVersion Include="Microsoft.Extensions.AI" Version="10.2.0" />
+    <PackageVersion Include="Microsoft.Extensions.AI.Abstractions" Version="10.2.0" />
+    <PackageVersion Include="Microsoft.Extensions.AI.OpenAI" Version="10.2.0-preview.1.26063.2" />
    <PackageVersion Include="Microsoft.Extensions.Caching.Memory" Version="10.0.0" />
    <PackageVersion Include="Microsoft.Extensions.Configuration" Version="10.0.0" />
    <PackageVersion Include="Microsoft.Extensions.Configuration.Binder" Version="10.0.0" />
@@ -74,11 +71,11 @@
    <PackageVersion Include="Microsoft.Extensions.Configuration.Json" Version="10.0.0" />
    <PackageVersion Include="Microsoft.Extensions.Configuration.UserSecrets" Version="10.0.0" />
    <PackageVersion Include="Microsoft.Extensions.DependencyInjection" Version="10.0.0" />
-    <PackageVersion Include="Microsoft.Extensions.DependencyInjection.Abstractions" Version="10.0.3" />
+    <PackageVersion Include="Microsoft.Extensions.DependencyInjection.Abstractions" Version="10.0.2" />
    <PackageVersion Include="Microsoft.Extensions.Hosting" Version="10.0.0" />
    <PackageVersion Include="Microsoft.Extensions.Http.Resilience" Version="10.0.0" />
    <PackageVersion Include="Microsoft.Extensions.Logging" Version="10.0.0" />
-    <PackageVersion Include="Microsoft.Extensions.Logging.Abstractions" Version="10.0.3" />
+    <PackageVersion Include="Microsoft.Extensions.Logging.Abstractions" Version="10.0.2" />
    <PackageVersion Include="Microsoft.Extensions.Logging.Console" Version="10.0.0" />
    <PackageVersion Include="Microsoft.Extensions.ServiceDiscovery" Version="10.0.0" />
    <PackageVersion Include="Microsoft.Extensions.VectorData.Abstractions" Version="9.7.0" />
@@ -92,7 +89,6 @@
    <PackageVersion Include="Microsoft.SemanticKernel.Agents.AzureAI" Version="1.67.0-preview" />
    <PackageVersion Include="Microsoft.SemanticKernel.Plugins.OpenApi" Version="1.67.0" />
    <!-- Agent SDKs -->
-    <PackageVersion Include="GitHub.Copilot.SDK" Version="0.1.25" />
    <PackageVersion Include="Microsoft.Agents.CopilotStudio.Client" Version="1.3.171-beta" />
    <!-- M365 Agents SDK -->
    <PackageVersion Include="AdaptiveCards" Version="3.1.0" />
@@ -102,7 +98,7 @@
    <PackageVersion Include="A2A" Version="0.3.3-preview" />
    <PackageVersion Include="A2A.AspNetCore" Version="0.3.3-preview" />
    <!-- MCP -->
-    <PackageVersion Include="ModelContextProtocol" Version="0.8.0-preview.1" />
+    <PackageVersion Include="ModelContextProtocol" Version="0.4.0-preview.3" />
    <!-- Inference SDKs -->
    <PackageVersion Include="AWSSDK.Extensions.Bedrock.MEAI" Version="4.0.5.1" />
    <PackageVersion Include="Microsoft.ML.OnnxRuntimeGenAI" Version="0.10.0" />
@@ -111,10 +107,10 @@
    <!-- Identity -->
    <PackageVersion Include="Microsoft.Identity.Client.Extensions.Msal" Version="4.78.0" />
    <!-- Workflows -->
-    <PackageVersion Include="Microsoft.Agents.ObjectModel" Version="2026.2.3.1" />
-    <PackageVersion Include="Microsoft.Agents.ObjectModel.Json" Version="2026.2.3.1" />
-    <PackageVersion Include="Microsoft.Agents.ObjectModel.PowerFx" Version="2026.2.3.1" />
-    <PackageVersion Include="Microsoft.PowerFx.Interpreter" Version="1.8.1" />
+    <PackageVersion Include="Microsoft.Bot.ObjectModel" Version="1.2025.1106.1" />
+    <PackageVersion Include="Microsoft.Bot.ObjectModel.Json" Version="1.2025.1106.1" />
+    <PackageVersion Include="Microsoft.Bot.ObjectModel.PowerFx" Version="1.2025.1106.1" />
+    <PackageVersion Include="Microsoft.PowerFx.Interpreter" Version="1.5.0-build.20251008-1002" />
    <!-- Durable Task -->
    <PackageVersion Include="Microsoft.DurableTask.Client" Version="1.18.0" />
    <PackageVersion Include="Microsoft.DurableTask.Client.AzureManaged" Version="1.18.0" />
@@ -147,7 +143,6 @@
    <!-- Symbols -->
    <PackageVersion Include="Microsoft.SourceLink.GitHub" Version="8.0.0" />
    <!-- Toolset -->
-    <PackageVersion Include="Microsoft.CodeAnalysis.Analyzers" Version="3.11.0" />
    <PackageVersion Include="Microsoft.CodeAnalysis.CSharp" Version="4.14.0" />
    <PackageVersion Include="Microsoft.CodeAnalysis.NetAnalyzers" Version="10.0.100" />
    <PackageReference Include="Microsoft.CodeAnalysis.NetAnalyzers">
@@ -11,16 +11,16 @@
 ### Basic Agent - .NET

 ```c#
+using System;
 using Azure.AI.OpenAI;
 using Azure.Identity;
 using Microsoft.Agents.AI;
-using OpenAI.Responses;

 var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT")!;
 var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME")!;

 var agent = new AzureOpenAIClient(new Uri(endpoint), new AzureCliCredential())
-    .GetResponsesClient(deploymentName)
+    .GetOpenAIResponseClient(deploymentName)
    .AsAIAgent(name: "HaikuBot", instructions: "You are an upbeat assistant that writes beautifully.");

 Console.WriteLine(await agent.RunAsync("Write a haiku about Microsoft Agent Framework."));
@@ -23,29 +23,29 @@
    <Project Path="samples/AGUIClientServer/AGUIDojoServer/AGUIDojoServer.csproj" />
    <Project Path="samples/AGUIClientServer/AGUIServer/AGUIServer.csproj" />
  </Folder>
-  <Folder Name="/Samples/Durable/" />
-  <Folder Name="/Samples/Durable/Agents/" />
-  <Folder Name="/Samples/Durable/Agents/AzureFunctions/">
-    <File Path="samples/Durable/Agents/AzureFunctions/.editorconfig" />
-    <File Path="samples/Durable/Agents/AzureFunctions/README.md" />
-    <Project Path="samples/Durable/Agents/AzureFunctions/01_SingleAgent/01_SingleAgent.csproj" />
-    <Project Path="samples/Durable/Agents/AzureFunctions/02_AgentOrchestration_Chaining/02_AgentOrchestration_Chaining.csproj" />
-    <Project Path="samples/Durable/Agents/AzureFunctions/03_AgentOrchestration_Concurrency/03_AgentOrchestration_Concurrency.csproj" />
-    <Project Path="samples/Durable/Agents/AzureFunctions/04_AgentOrchestration_Conditionals/04_AgentOrchestration_Conditionals.csproj" />
-    <Project Path="samples/Durable/Agents/AzureFunctions/05_AgentOrchestration_HITL/05_AgentOrchestration_HITL.csproj" />
-    <Project Path="samples/Durable/Agents/AzureFunctions/06_LongRunningTools/06_LongRunningTools.csproj" />
-    <Project Path="samples/Durable/Agents/AzureFunctions/07_AgentAsMcpTool/07_AgentAsMcpTool.csproj" />
-    <Project Path="samples/Durable/Agents/AzureFunctions/08_ReliableStreaming/08_ReliableStreaming.csproj" />
+  <Folder Name="/Samples/AzureFunctions/">
+    <File Path="samples/AzureFunctions/.editorconfig" />
+    <File Path="samples/AzureFunctions/README.md" />
+    <Project Path="samples/AzureFunctions/01_SingleAgent/01_SingleAgent.csproj" />
+    <Project Path="samples/AzureFunctions/02_AgentOrchestration_Chaining/02_AgentOrchestration_Chaining.csproj" />
+    <Project Path="samples/AzureFunctions/03_AgentOrchestration_Concurrency/03_AgentOrchestration_Concurrency.csproj" />
+    <Project Path="samples/AzureFunctions/04_AgentOrchestration_Conditionals/04_AgentOrchestration_Conditionals.csproj" />
+    <Project Path="samples/AzureFunctions/05_AgentOrchestration_HITL/05_AgentOrchestration_HITL.csproj" />
+    <Project Path="samples/AzureFunctions/06_LongRunningTools/06_LongRunningTools.csproj" />
+    <Project Path="samples/AzureFunctions/07_AgentAsMcpTool/07_AgentAsMcpTool.csproj" />
+    <Project Path="samples/AzureFunctions/08_ReliableStreaming/08_ReliableStreaming.csproj" />
  </Folder>
-  <Folder Name="/Samples/Durable/Agents/ConsoleApps/">
-    <File Path="samples/Durable/Agents/ConsoleApps/README.md" />
-    <Project Path="samples/Durable/Agents/ConsoleApps/01_SingleAgent/01_SingleAgent.csproj" />
-    <Project Path="samples/Durable/Agents/ConsoleApps/02_AgentOrchestration_Chaining/02_AgentOrchestration_Chaining.csproj" />
-    <Project Path="samples/Durable/Agents/ConsoleApps/03_AgentOrchestration_Concurrency/03_AgentOrchestration_Concurrency.csproj" />
-    <Project Path="samples/Durable/Agents/ConsoleApps/04_AgentOrchestration_Conditionals/04_AgentOrchestration_Conditionals.csproj" />
-    <Project Path="samples/Durable/Agents/ConsoleApps/05_AgentOrchestration_HITL/05_AgentOrchestration_HITL.csproj" />
-    <Project Path="samples/Durable/Agents/ConsoleApps/06_LongRunningTools/06_LongRunningTools.csproj" />
-    <Project Path="samples/Durable/Agents/ConsoleApps/07_ReliableStreaming/07_ReliableStreaming.csproj" />
+  <Folder Name="/Samples/DurableAgents/">
+    <File Path="samples/DurableAgents/ConsoleApps/README.md" />
+  </Folder>
+  <Folder Name="/Samples/DurableAgents/ConsoleApps/">
+    <Project Path="samples/DurableAgents/ConsoleApps/01_SingleAgent/01_SingleAgent.csproj" />
+    <Project Path="samples/DurableAgents/ConsoleApps/02_AgentOrchestration_Chaining/02_AgentOrchestration_Chaining.csproj" />
+    <Project Path="samples/DurableAgents/ConsoleApps/03_AgentOrchestration_Concurrency/03_AgentOrchestration_Concurrency.csproj" />
+    <Project Path="samples/DurableAgents/ConsoleApps/04_AgentOrchestration_Conditionals/04_AgentOrchestration_Conditionals.csproj" />
+    <Project Path="samples/DurableAgents/ConsoleApps/05_AgentOrchestration_HITL/05_AgentOrchestration_HITL.csproj" />
+    <Project Path="samples/DurableAgents/ConsoleApps/06_LongRunningTools/06_LongRunningTools.csproj" />
+    <Project Path="samples/DurableAgents/ConsoleApps/07_ReliableStreaming/07_ReliableStreaming.csproj" />
  </Folder>
  <Folder Name="/Samples/GettingStarted/">
    <File Path="samples/GettingStarted/README.md" />
@@ -65,7 +65,6 @@
    <Project Path="samples/GettingStarted/AgentProviders/Agent_With_AzureOpenAIChatCompletion/Agent_With_AzureOpenAIChatCompletion.csproj" />
    <Project Path="samples/GettingStarted/AgentProviders/Agent_With_AzureOpenAIResponses/Agent_With_AzureOpenAIResponses.csproj" />
    <Project Path="samples/GettingStarted/AgentProviders/Agent_With_CustomImplementation/Agent_With_CustomImplementation.csproj" />
-    <Project Path="samples/GettingStarted/AgentProviders/Agent_With_GitHubCopilot/Agent_With_GitHubCopilot.csproj" />
    <Project Path="samples/GettingStarted/AgentProviders/Agent_With_GoogleGemini/Agent_With_GoogleGemini.csproj" />
    <Project Path="samples/GettingStarted/AgentProviders/Agent_With_Ollama/Agent_With_Ollama.csproj" />
    <Project Path="samples/GettingStarted/AgentProviders/Agent_With_ONNX/Agent_With_ONNX.csproj" />
@@ -81,7 +80,7 @@
    <Project Path="samples/GettingStarted/Agents/Agent_Step04_UsingFunctionToolsWithApprovals/Agent_Step04_UsingFunctionToolsWithApprovals.csproj" />
    <Project Path="samples/GettingStarted/Agents/Agent_Step05_StructuredOutput/Agent_Step05_StructuredOutput.csproj" />
    <Project Path="samples/GettingStarted/Agents/Agent_Step06_PersistedConversations/Agent_Step06_PersistedConversations.csproj" />
-    <Project Path="samples/GettingStarted/Agents/Agent_Step07_3rdPartyChatHistoryStorage/Agent_Step07_3rdPartyChatHistoryStorage.csproj" />
+    <Project Path="samples/GettingStarted/Agents/Agent_Step07_3rdPartyThreadStorage/Agent_Step07_3rdPartyThreadStorage.csproj" />
    <Project Path="samples/GettingStarted/Agents/Agent_Step08_Observability/Agent_Step08_Observability.csproj" />
    <Project Path="samples/GettingStarted/Agents/Agent_Step09_DependencyInjection/Agent_Step09_DependencyInjection.csproj" />
    <Project Path="samples/GettingStarted/Agents/Agent_Step10_AsMcpTool/Agent_Step10_AsMcpTool.csproj" />
@@ -96,10 +95,6 @@
    <Project Path="samples/GettingStarted/Agents/Agent_Step19_Declarative/Agent_Step19_Declarative.csproj" />
    <Project Path="samples/GettingStarted/Agents/Agent_Step20_AdditionalAIContext/Agent_Step20_AdditionalAIContext.csproj" />
  </Folder>
-  <Folder Name="/Samples/GettingStarted/AgentSkills/">
-    <File Path="samples/GettingStarted/AgentSkills/README.md" />
-    <Project Path="samples/GettingStarted/AgentSkills/Agent_Step01_BasicSkills/Agent_Step01_BasicSkills.csproj" />
-  </Folder>
  <Folder Name="/Samples/GettingStarted/DeclarativeAgents/">
    <Project Path="samples/GettingStarted/DeclarativeAgents/ChatClient/DeclarativeChatClientAgents.csproj" />
  </Folder>
@@ -135,14 +130,12 @@
    <Project Path="samples/GettingStarted/AgentWithAnthropic/Agent_Anthropic_Step01_Running/Agent_Anthropic_Step01_Running.csproj" />
    <Project Path="samples/GettingStarted/AgentWithAnthropic/Agent_Anthropic_Step02_Reasoning/Agent_Anthropic_Step02_Reasoning.csproj" />
    <Project Path="samples/GettingStarted/AgentWithAnthropic/Agent_Anthropic_Step03_UsingFunctionTools/Agent_Anthropic_Step03_UsingFunctionTools.csproj" />
-    <Project Path="samples/GettingStarted/AgentWithAnthropic/Agent_Anthropic_Step04_UsingSkills/Agent_Anthropic_Step04_UsingSkills.csproj" />
  </Folder>
  <Folder Name="/Samples/GettingStarted/AgentWithMemory/">
    <File Path="samples/GettingStarted/AgentWithMemory/README.md" />
    <Project Path="samples/GettingStarted/AgentWithMemory/AgentWithMemory_Step01_ChatHistoryMemory/AgentWithMemory_Step01_ChatHistoryMemory.csproj" />
    <Project Path="samples/GettingStarted/AgentWithMemory/AgentWithMemory_Step02_MemoryUsingMem0/AgentWithMemory_Step02_MemoryUsingMem0.csproj" />
    <Project Path="samples/GettingStarted/AgentWithMemory/AgentWithMemory_Step03_CustomMemory/AgentWithMemory_Step03_CustomMemory.csproj" />
-    <Project Path="samples/GettingStarted/AgentWithMemory/AgentWithMemory_Step04_MemoryUsingFoundry/AgentWithMemory_Step04_MemoryUsingFoundry.csproj" />
  </Folder>
  <Folder Name="/Samples/GettingStarted/AgentWithOpenAI/">
    <File Path="samples/GettingStarted/AgentWithOpenAI/README.md" />
@@ -181,10 +174,6 @@
    <Project Path="samples/GettingStarted/FoundryAgents/FoundryAgents_Step13_Plugins/FoundryAgents_Step13_Plugins.csproj" />
    <Project Path="samples/GettingStarted/FoundryAgents/FoundryAgents_Step14_CodeInterpreter/FoundryAgents_Step14_CodeInterpreter.csproj" />
    <Project Path="samples/GettingStarted/FoundryAgents/FoundryAgents_Step15_ComputerUse/FoundryAgents_Step15_ComputerUse.csproj" />
-    <Project Path="samples/GettingStarted/FoundryAgents/FoundryAgents_Step19_OpenAPITools/FoundryAgents_Step19_OpenAPITools.csproj" />
-    <Project Path="samples/GettingStarted/FoundryAgents/FoundryAgents_Step18_FileSearch/FoundryAgents_Step18_FileSearch.csproj" />
-    <Project Path="samples/GettingStarted/FoundryAgents/FoundryAgents_Evaluations_Step01_RedTeaming/FoundryAgents_Evaluations_Step01_RedTeaming.csproj" />
-    <Project Path="samples/GettingStarted/FoundryAgents/FoundryAgents_Evaluations_Step02_SelfReflection/FoundryAgents_Evaluations_Step02_SelfReflection.csproj" />
  </Folder>
  <Folder Name="/Samples/GettingStarted/ModelContextProtocol/">
    <File Path="samples/GettingStarted/ModelContextProtocol/README.md" />
@@ -222,7 +211,6 @@
    <Project Path="samples/GettingStarted/Workflows/Declarative/Marketing/Marketing.csproj" />
    <Project Path="samples/GettingStarted/Workflows/Declarative/StudentTeacher/StudentTeacher.csproj" />
    <Project Path="samples/GettingStarted/Workflows/Declarative/ToolApproval/ToolApproval.csproj" />
-    <Project Path="samples/GettingStarted/Workflows/Declarative/InvokeFunctionTool/InvokeFunctionTool.csproj" />
  </Folder>
  <Folder Name="/Samples/GettingStarted/Workflows/Declarative/Examples/">
    <File Path="../workflow-samples/CustomerSupport.yaml" />
@@ -241,7 +229,6 @@
  <Folder Name="/Samples/GettingStarted/Workflows/Agents/">
    <Project Path="samples/GettingStarted/Workflows/Agents/CustomAgentExecutors/CustomAgentExecutors.csproj" />
    <Project Path="samples/GettingStarted/Workflows/Agents/FoundryAgent/FoundryAgent.csproj" />
-    <Project Path="samples/GettingStarted/Workflows/Agents/GroupChatToolApproval/GroupChatToolApproval.csproj" />
    <Project Path="samples/GettingStarted/Workflows/Agents/WorkflowAsAnAgent/WorkflowAsAnAgent.csproj" />
  </Folder>
  <Folder Name="/Samples/GettingStarted/Workflows/Checkpoint/">
@@ -312,11 +299,6 @@
    <File Path="../docs/decisions/0007-agent-filtering-middleware.md" />
    <File Path="../docs/decisions/0008-python-subpackages.md" />
    <File Path="../docs/decisions/0009-support-long-running-operations.md" />
-    <File Path="../docs/decisions/0010-ag-ui-support.md" />
-    <File Path="../docs/decisions/0011-create-get-agent-api.md" />
-    <File Path="../docs/decisions/0012-python-typeddict-options.md" />
-    <File Path="../docs/decisions/0013-python-get-response-simplification.md" />
-    <File Path="../docs/decisions/0014-feature-collections.md" />
    <File Path="../docs/decisions/adr-short-template.md" />
    <File Path="../docs/decisions/adr-template.md" />
    <File Path="../docs/decisions/README.md" />
@@ -381,10 +363,6 @@
    <File Path="src/Shared/Demos/README.md" />
    <File Path="src/Shared/Demos/SampleEnvironment.cs" />
  </Folder>
-  <Folder Name="/Solution Items/src/Shared/DiagnosticIds/">
-    <File Path="src/Shared/DiagnosticIds/DiagnosticsIds.cs" />
-    <File Path="src/Shared/DiagnosticIds/README.md" />
-  </Folder>
  <Folder Name="/Solution Items/src/Shared/IntegrationTests/">
    <File Path="src/Shared/IntegrationTests/AnthropicConfiguration.cs" />
    <File Path="src/Shared/IntegrationTests/AzureAIConfiguration.cs" />
@@ -403,9 +381,6 @@
    <File Path="src/Shared/Throw/README.md" />
    <File Path="src/Shared/Throw/Throw.cs" />
  </Folder>
-  <Folder Name="/Solution Items/src/Shared/StructuredOutput/">
-    <File Path="src/Shared/StructuredOutput/StructuredOutputSchemaUtilities.cs" />
-  </Folder>
  <Folder Name="/Solution Items/tests/">
    <File Path="tests/.editorconfig" />
    <File Path="tests/Directory.Build.props" />
@@ -422,20 +397,17 @@
    <Project Path="src/Microsoft.Agents.AI.Declarative/Microsoft.Agents.AI.Declarative.csproj" />
    <Project Path="src/Microsoft.Agents.AI.DevUI/Microsoft.Agents.AI.DevUI.csproj" />
    <Project Path="src/Microsoft.Agents.AI.DurableTask/Microsoft.Agents.AI.DurableTask.csproj" />
-    <Project Path="src/Microsoft.Agents.AI.GitHub.Copilot/Microsoft.Agents.AI.GitHub.Copilot.csproj" />
    <Project Path="src/Microsoft.Agents.AI.Hosting.A2A.AspNetCore/Microsoft.Agents.AI.Hosting.A2A.AspNetCore.csproj" />
    <Project Path="src/Microsoft.Agents.AI.Hosting.A2A/Microsoft.Agents.AI.Hosting.A2A.csproj" />
    <Project Path="src/Microsoft.Agents.AI.Hosting.AGUI.AspNetCore/Microsoft.Agents.AI.Hosting.AGUI.AspNetCore.csproj" />
    <Project Path="src/Microsoft.Agents.AI.Hosting.AzureFunctions/Microsoft.Agents.AI.Hosting.AzureFunctions.csproj" />
    <Project Path="src/Microsoft.Agents.AI.Hosting.OpenAI/Microsoft.Agents.AI.Hosting.OpenAI.csproj" />
    <Project Path="src/Microsoft.Agents.AI.Hosting/Microsoft.Agents.AI.Hosting.csproj" />
-    <Project Path="src/Microsoft.Agents.AI.FoundryMemory/Microsoft.Agents.AI.FoundryMemory.csproj" />
    <Project Path="src/Microsoft.Agents.AI.Mem0/Microsoft.Agents.AI.Mem0.csproj" />
    <Project Path="src/Microsoft.Agents.AI.OpenAI/Microsoft.Agents.AI.OpenAI.csproj" />
    <Project Path="src/Microsoft.Agents.AI.Purview/Microsoft.Agents.AI.Purview.csproj" />
    <Project Path="src/Microsoft.Agents.AI.Workflows.Declarative.AzureAI/Microsoft.Agents.AI.Workflows.Declarative.AzureAI.csproj" />
    <Project Path="src/Microsoft.Agents.AI.Workflows.Declarative/Microsoft.Agents.AI.Workflows.Declarative.csproj" />
-    <Project Path="src/Microsoft.Agents.AI.Workflows.Generators/Microsoft.Agents.AI.Workflows.Generators.csproj" />
    <Project Path="src/Microsoft.Agents.AI.Workflows/Microsoft.Agents.AI.Workflows.csproj" />
    <Project Path="src/Microsoft.Agents.AI/Microsoft.Agents.AI.csproj" />
  </Folder>
@@ -447,11 +419,9 @@
    <Project Path="tests/AzureAIAgentsPersistent.IntegrationTests/AzureAIAgentsPersistent.IntegrationTests.csproj" />
    <Project Path="tests/CopilotStudio.IntegrationTests/CopilotStudio.IntegrationTests.csproj" />
    <Project Path="tests/Microsoft.Agents.AI.DurableTask.IntegrationTests/Microsoft.Agents.AI.DurableTask.IntegrationTests.csproj" />
-    <Project Path="tests/Microsoft.Agents.AI.GitHub.Copilot.IntegrationTests/Microsoft.Agents.AI.GitHub.Copilot.IntegrationTests.csproj" />
    <Project Path="tests/Microsoft.Agents.AI.Hosting.AGUI.AspNetCore.IntegrationTests/Microsoft.Agents.AI.Hosting.AGUI.AspNetCore.IntegrationTests.csproj" />
    <Project Path="tests/Microsoft.Agents.AI.Hosting.AzureFunctions.IntegrationTests/Microsoft.Agents.AI.Hosting.AzureFunctions.IntegrationTests.csproj" />
    <Project Path="tests/Microsoft.Agents.AI.Mem0.IntegrationTests/Microsoft.Agents.AI.Mem0.IntegrationTests.csproj" />
-    <Project Path="tests/Microsoft.Agents.AI.FoundryMemory.IntegrationTests/Microsoft.Agents.AI.FoundryMemory.IntegrationTests.csproj" />
    <Project Path="tests/Microsoft.Agents.AI.Workflows.Declarative.IntegrationTests/Microsoft.Agents.AI.Workflows.Declarative.IntegrationTests.csproj" />
    <Project Path="tests/OpenAIAssistant.IntegrationTests/OpenAIAssistant.IntegrationTests.csproj" />
    <Project Path="tests/OpenAIChatCompletion.IntegrationTests/OpenAIChatCompletion.IntegrationTests.csproj" />
@@ -468,19 +438,16 @@
    <Project Path="tests/Microsoft.Agents.AI.Declarative.UnitTests/Microsoft.Agents.AI.Declarative.UnitTests.csproj" />
    <Project Path="tests/Microsoft.Agents.AI.DevUI.UnitTests/Microsoft.Agents.AI.DevUI.UnitTests.csproj" />
    <Project Path="tests/Microsoft.Agents.AI.DurableTask.UnitTests/Microsoft.Agents.AI.DurableTask.UnitTests.csproj" />
-    <Project Path="tests/Microsoft.Agents.AI.GitHub.Copilot.UnitTests/Microsoft.Agents.AI.GitHub.Copilot.UnitTests.csproj" />
    <Project Path="tests/Microsoft.Agents.AI.Hosting.A2A.UnitTests/Microsoft.Agents.AI.Hosting.A2A.UnitTests.csproj" />
    <Project Path="tests/Microsoft.Agents.AI.Hosting.AGUI.AspNetCore.UnitTests/Microsoft.Agents.AI.Hosting.AGUI.AspNetCore.UnitTests.csproj" />
    <Project Path="tests/Microsoft.Agents.AI.Hosting.AzureFunctions.UnitTests/Microsoft.Agents.AI.Hosting.AzureFunctions.UnitTests.csproj" />
    <Project Path="tests/Microsoft.Agents.AI.Hosting.OpenAI.UnitTests/Microsoft.Agents.AI.Hosting.OpenAI.UnitTests.csproj" />
    <Project Path="tests/Microsoft.Agents.AI.Hosting.UnitTests/Microsoft.Agents.AI.Hosting.UnitTests.csproj" />
-    <Project Path="tests/Microsoft.Agents.AI.FoundryMemory.UnitTests/Microsoft.Agents.AI.FoundryMemory.UnitTests.csproj" />
    <Project Path="tests/Microsoft.Agents.AI.Mem0.UnitTests/Microsoft.Agents.AI.Mem0.UnitTests.csproj" />
    <Project Path="tests/Microsoft.Agents.AI.OpenAI.UnitTests/Microsoft.Agents.AI.OpenAI.UnitTests.csproj" />
    <Project Path="tests/Microsoft.Agents.AI.Purview.UnitTests/Microsoft.Agents.AI.Purview.UnitTests.csproj" />
    <Project Path="tests/Microsoft.Agents.AI.UnitTests/Microsoft.Agents.AI.UnitTests.csproj" />
    <Project Path="tests/Microsoft.Agents.AI.Workflows.Declarative.UnitTests/Microsoft.Agents.AI.Workflows.Declarative.UnitTests.csproj" />
-    <Project Path="tests/Microsoft.Agents.AI.Workflows.Generators.UnitTests/Microsoft.Agents.AI.Workflows.Generators.UnitTests.csproj" />
    <Project Path="tests/Microsoft.Agents.AI.Workflows.UnitTests/Microsoft.Agents.AI.Workflows.UnitTests.csproj" />
  </Folder>
 </Solution>
@@ -6,7 +6,6 @@
      "src\\Microsoft.Agents.AI.Abstractions\\Microsoft.Agents.AI.Abstractions.csproj",
      "src\\Microsoft.Agents.AI.AGUI\\Microsoft.Agents.AI.AGUI.csproj",
      "src\\Microsoft.Agents.AI.Anthropic\\Microsoft.Agents.AI.Anthropic.csproj",
-      "src\\Microsoft.Agents.AI.GitHub.Copilot\\Microsoft.Agents.AI.GitHub.Copilot.csproj",
      "src\\Microsoft.Agents.AI.AzureAI.Persistent\\Microsoft.Agents.AI.AzureAI.Persistent.csproj",
      "src\\Microsoft.Agents.AI.AzureAI\\Microsoft.Agents.AI.AzureAI.csproj",
      "src\\Microsoft.Agents.AI.CopilotStudio\\Microsoft.Agents.AI.CopilotStudio.csproj",
@@ -25,7 +24,6 @@
      "src\\Microsoft.Agents.AI.Purview\\Microsoft.Agents.AI.Purview.csproj",
      "src\\Microsoft.Agents.AI.Workflows.Declarative.AzureAI\\Microsoft.Agents.AI.Workflows.Declarative.AzureAI.csproj",
      "src\\Microsoft.Agents.AI.Workflows.Declarative\\Microsoft.Agents.AI.Workflows.Declarative.csproj",
-      "src\\Microsoft.Agents.AI.Workflows.Generators\\Microsoft.Agents.AI.Workflows.Generators.csproj",
      "src\\Microsoft.Agents.AI.Workflows\\Microsoft.Agents.AI.Workflows.csproj",
      "src\\Microsoft.Agents.AI\\Microsoft.Agents.AI.csproj"
    ]
@@ -20,10 +20,4 @@
  <ItemGroup Condition="'$(InjectSharedFoundryAgents)' == 'true'">
    <Compile Include="$(MSBuildThisFileDirectory)\..\..\src\Shared\Foundry\Agents\*.cs" LinkBase="Shared\Foundry" />
  </ItemGroup>
-  <ItemGroup Condition="'$(InjectSharedStructuredOutput)' == 'true'">
-    <Compile Include="$(MSBuildThisFileDirectory)\..\..\src\Shared\StructuredOutput\*.cs" LinkBase="Shared\StructuredOutput" />
-  </ItemGroup>
-  <ItemGroup Condition="'$(InjectSharedDiagnosticIds)' == 'true'">
-    <Compile Include="$(MSBuildThisFileDirectory)\..\..\src\Shared\DiagnosticIds\*.cs" LinkBase="Shared\DiagnosticIds" />
-  </ItemGroup>
 </Project>
@@ -2,11 +2,9 @@
  <PropertyGroup>
    <!-- Central version prefix - applies to all nuget packages. -->
    <VersionPrefix>1.0.0</VersionPrefix>
-    <RCNumber>1</RCNumber>
-    <PackageVersion Condition="'$(IsReleaseCandidate)' == 'true'">$(VersionPrefix)-rc$(RCNumber)</PackageVersion>
-    <PackageVersion Condition="'$(IsReleaseCandidate)' != 'true' AND '$(VersionSuffix)' != ''">$(VersionPrefix)-$(VersionSuffix).260219.1</PackageVersion>
-    <PackageVersion Condition="'$(IsReleaseCandidate)' != 'true' AND '$(VersionSuffix)' == ''">$(VersionPrefix)-preview.260219.1</PackageVersion>
-    <GitTag>1.0.0-rc1</GitTag>
+    <PackageVersion Condition="'$(VersionSuffix)' != ''">$(VersionPrefix)-$(VersionSuffix).260121.1</PackageVersion>
+    <PackageVersion Condition="'$(VersionSuffix)' == ''">$(VersionPrefix)-preview.260121.1</PackageVersion>
+    <GitTag>1.0.0-preview.260121.1</GitTag>

    <Configurations>Debug;Release;Publish</Configurations>
    <IsPackable>true</IsPackable>
@@ -42,7 +42,7 @@ public static class Program
        // Create the Host agent
        var hostAgent = new HostClientAgent(loggerFactory);
        await hostAgent.InitializeAgentAsync(modelId, apiKey, agentUrls!.Split(";"));
-        AgentSession session = await hostAgent.Agent!.CreateSessionAsync(cancellationToken);
+        AgentThread thread = await hostAgent.Agent!.GetNewThreadAsync(cancellationToken);
        try
        {
            while (true)
@@ -61,7 +61,7 @@ public static class Program
                    break;
                }

-                var agentResponse = await hostAgent.Agent!.RunAsync(message, session, cancellationToken: cancellationToken);
+                var agentResponse = await hostAgent.Agent!.RunAsync(message, thread, cancellationToken: cancellationToken);
                foreach (var chatMessage in agentResponse.Messages)
                {
                    Console.ForegroundColor = ConsoleColor.Cyan;
@@ -14,10 +14,7 @@ internal static class HostAgentFactory
 {
    internal static async Task<(AIAgent, AgentCard)> CreateFoundryHostAgentAsync(string agentType, string model, string endpoint, string assistantId, IList<AITool>? tools = null)
    {
-        // WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
-        // In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
-        // latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
-        var persistentAgentsClient = new PersistentAgentsClient(endpoint, new DefaultAzureCredential());
+        var persistentAgentsClient = new PersistentAgentsClient(endpoint, new AzureCliCredential());
        PersistentAgent persistentAgent = await persistentAgentsClient.Administration.GetAgentAsync(assistantId);

        AIAgent agent = await persistentAgentsClient
@@ -88,7 +88,7 @@ public static class Program
            description: "AG-UI Client Agent",
            tools: [changeBackground, readClientClimateSensors]);

-        AgentSession session = await agent.CreateSessionAsync(cancellationToken);
+        AgentThread thread = await agent.GetNewThreadAsync(cancellationToken);
        List<ChatMessage> messages = [new(ChatRole.System, "You are a helpful assistant.")];
        try
        {
@@ -112,23 +112,23 @@ public static class Program

                // Call RunStreamingAsync to get streaming updates
                bool isFirstUpdate = true;
-                string? sessionId = null;
+                string? threadId = null;
                var updates = new List<ChatResponseUpdate>();
-                await foreach (AgentResponseUpdate update in agent.RunStreamingAsync(messages, session, cancellationToken: cancellationToken))
+                await foreach (AgentResponseUpdate update in agent.RunStreamingAsync(messages, thread, cancellationToken: cancellationToken))
                {
                    // Use AsChatResponseUpdate to access ChatResponseUpdate properties
                    ChatResponseUpdate chatUpdate = update.AsChatResponseUpdate();
                    updates.Add(chatUpdate);
                    if (chatUpdate.ConversationId != null)
                    {
-                        sessionId = chatUpdate.ConversationId;
+                        threadId = chatUpdate.ConversationId;
                    }

                    // Display run started information from the first update
-                    if (isFirstUpdate && sessionId != null && update.ResponseId != null)
+                    if (isFirstUpdate && threadId != null && update.ResponseId != null)
                    {
                        Console.ForegroundColor = ConsoleColor.Yellow;
-                        Console.WriteLine($"\n[Run Started - Session: {sessionId}, Run: {update.ResponseId}]");
+                        Console.WriteLine($"\n[Run Started - Thread: {threadId}, Run: {update.ResponseId}]");
                        Console.ResetColor();
                        isFirstUpdate = false;
                    }
@@ -177,7 +177,7 @@ public static class Program
                    var lastUpdate = updates[^1];
                    Console.ForegroundColor = ConsoleColor.Yellow;
                    Console.WriteLine();
-                    Console.WriteLine($"[Run Ended - Session: {sessionId}, Run: {lastUpdate.ResponseId}]");
+                    Console.WriteLine($"[Run Ended - Thread: {threadId}, Run: {lastUpdate.ResponseId}]");
                    Console.ResetColor();
                }
                messages.Clear();
@@ -19,21 +19,21 @@ internal sealed class AgenticUIAgent : DelegatingAIAgent
        this._jsonSerializerOptions = jsonSerializerOptions;
    }

-    protected override Task<AgentResponse> RunCoreAsync(IEnumerable<ChatMessage> messages, AgentSession? session = null, AgentRunOptions? options = null, CancellationToken cancellationToken = default)
+    protected override Task<AgentResponse> RunCoreAsync(IEnumerable<ChatMessage> messages, AgentThread? thread = null, AgentRunOptions? options = null, CancellationToken cancellationToken = default)
    {
-        return this.RunCoreStreamingAsync(messages, session, options, cancellationToken).ToAgentResponseAsync(cancellationToken);
+        return this.RunCoreStreamingAsync(messages, thread, options, cancellationToken).ToAgentResponseAsync(cancellationToken);
    }

    protected override async IAsyncEnumerable<AgentResponseUpdate> RunCoreStreamingAsync(
        IEnumerable<ChatMessage> messages,
-        AgentSession? session = null,
+        AgentThread? thread = null,
        AgentRunOptions? options = null,
        [EnumeratorCancellation] CancellationToken cancellationToken = default)
    {
        // Track function calls that should trigger state events
        var trackedFunctionCalls = new Dictionary<string, FunctionCallContent>();

-        await foreach (var update in this.InnerAgent.RunStreamingAsync(messages, session, options, cancellationToken).ConfigureAwait(false))
+        await foreach (var update in this.InnerAgent.RunStreamingAsync(messages, thread, options, cancellationToken).ConfigureAwait(false))
        {
            // Process contents: track function calls and emit state events for results
            List<AIContent> stateEventsToEmit = new();
@@ -24,9 +24,6 @@ internal static class ChatClientAgentFactory
        string endpoint = configuration["AZURE_OPENAI_ENDPOINT"] ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
        s_deploymentName = configuration["AZURE_OPENAI_DEPLOYMENT_NAME"] ?? throw new InvalidOperationException("AZURE_OPENAI_DEPLOYMENT_NAME is not set.");

-        // WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
-        // In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
-        // latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
        s_azureOpenAIClient = new AzureOpenAIClient(
            new Uri(endpoint),
            new DefaultAzureCredential());
@@ -20,21 +20,21 @@ internal sealed class PredictiveStateUpdatesAgent : DelegatingAIAgent
        this._jsonSerializerOptions = jsonSerializerOptions;
    }

-    protected override Task<AgentResponse> RunCoreAsync(IEnumerable<ChatMessage> messages, AgentSession? session = null, AgentRunOptions? options = null, CancellationToken cancellationToken = default)
+    protected override Task<AgentResponse> RunCoreAsync(IEnumerable<ChatMessage> messages, AgentThread? thread = null, AgentRunOptions? options = null, CancellationToken cancellationToken = default)
    {
-        return this.RunCoreStreamingAsync(messages, session, options, cancellationToken).ToAgentResponseAsync(cancellationToken);
+        return this.RunCoreStreamingAsync(messages, thread, options, cancellationToken).ToAgentResponseAsync(cancellationToken);
    }

    protected override async IAsyncEnumerable<AgentResponseUpdate> RunCoreStreamingAsync(
        IEnumerable<ChatMessage> messages,
-        AgentSession? session = null,
+        AgentThread? thread = null,
        AgentRunOptions? options = null,
        [EnumeratorCancellation] CancellationToken cancellationToken = default)
    {
        // Track the last emitted document state to avoid duplicates
        string? lastEmittedDocument = null;

-        await foreach (var update in this.InnerAgent.RunStreamingAsync(messages, session, options, cancellationToken).ConfigureAwait(false))
+        await foreach (var update in this.InnerAgent.RunStreamingAsync(messages, thread, options, cancellationToken).ConfigureAwait(false))
        {
            // Check if we're seeing a write_document tool call and emit predictive state
            bool hasToolCall = false;
@@ -19,21 +19,21 @@ internal sealed class SharedStateAgent : DelegatingAIAgent
        this._jsonSerializerOptions = jsonSerializerOptions;
    }

-    protected override Task<AgentResponse> RunCoreAsync(IEnumerable<ChatMessage> messages, AgentSession? session = null, AgentRunOptions? options = null, CancellationToken cancellationToken = default)
+    protected override Task<AgentResponse> RunCoreAsync(IEnumerable<ChatMessage> messages, AgentThread? thread = null, AgentRunOptions? options = null, CancellationToken cancellationToken = default)
    {
-        return this.RunCoreStreamingAsync(messages, session, options, cancellationToken).ToAgentResponseAsync(cancellationToken);
+        return this.RunCoreStreamingAsync(messages, thread, options, cancellationToken).ToAgentResponseAsync(cancellationToken);
    }

    protected override async IAsyncEnumerable<AgentResponseUpdate> RunCoreStreamingAsync(
        IEnumerable<ChatMessage> messages,
-        AgentSession? session = null,
+        AgentThread? thread = null,
        AgentRunOptions? options = null,
        [EnumeratorCancellation] CancellationToken cancellationToken = default)
    {
        if (options is not ChatClientAgentRunOptions { ChatOptions.AdditionalProperties: { } properties } chatRunOptions ||
            !properties.TryGetValue("ag_ui_state", out JsonElement state))
        {
-            await foreach (var update in this.InnerAgent.RunStreamingAsync(messages, session, options, cancellationToken).ConfigureAwait(false))
+            await foreach (var update in this.InnerAgent.RunStreamingAsync(messages, thread, options, cancellationToken).ConfigureAwait(false))
            {
                yield return update;
            }
@@ -64,7 +64,7 @@ internal sealed class SharedStateAgent : DelegatingAIAgent
        var firstRunMessages = messages.Append(stateUpdateMessage);

        var allUpdates = new List<AgentResponseUpdate>();
-        await foreach (var update in this.InnerAgent.RunStreamingAsync(firstRunMessages, session, firstRunOptions, cancellationToken).ConfigureAwait(false))
+        await foreach (var update in this.InnerAgent.RunStreamingAsync(firstRunMessages, thread, firstRunOptions, cancellationToken).ConfigureAwait(false))
        {
            allUpdates.Add(update);

@@ -78,7 +78,7 @@ internal sealed class SharedStateAgent : DelegatingAIAgent

        var response = allUpdates.ToAgentResponse();

-        if (TryDeserialize(response.Text, this._jsonSerializerOptions, out JsonElement stateSnapshot))
+        if (response.TryDeserialize(this._jsonSerializerOptions, out JsonElement stateSnapshot))
        {
            byte[] stateBytes = JsonSerializer.SerializeToUtf8Bytes(
                stateSnapshot,
@@ -98,30 +98,9 @@ internal sealed class SharedStateAgent : DelegatingAIAgent
                ChatRole.System,
                [new TextContent("Please provide a concise summary of the state changes in at most two sentences.")]));

-        await foreach (var update in this.InnerAgent.RunStreamingAsync(secondRunMessages, session, options, cancellationToken).ConfigureAwait(false))
+        await foreach (var update in this.InnerAgent.RunStreamingAsync(secondRunMessages, thread, options, cancellationToken).ConfigureAwait(false))
        {
            yield return update;
        }
    }
-
-    private static bool TryDeserialize<T>(string json, JsonSerializerOptions jsonSerializerOptions, out T structuredOutput)
-    {
-        try
-        {
-            T? result = JsonSerializer.Deserialize<T>(json, jsonSerializerOptions);
-            if (result is null)
-            {
-                structuredOutput = default!;
-                return false;
-            }
-
-            structuredOutput = result;
-            return true;
-        }
-        catch
-        {
-            structuredOutput = default!;
-            return false;
-        }
-    }
 }
@@ -19,9 +19,6 @@ string endpoint = builder.Configuration["AZURE_OPENAI_ENDPOINT"] ?? throw new In
 string deploymentName = builder.Configuration["AZURE_OPENAI_DEPLOYMENT_NAME"] ?? throw new InvalidOperationException("AZURE_OPENAI_DEPLOYMENT_NAME is not set.");

 // Create the AI agent with tools
-// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
-// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
-// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
 var agent = new AzureOpenAIClient(
        new Uri(endpoint),
        new DefaultAzureCredential())
@@ -19,9 +19,6 @@ string endpoint = builder.Configuration["AZURE_OPENAI_ENDPOINT"] ?? throw new In
 string deploymentName = builder.Configuration["AZURE_OPENAI_DEPLOYMENT_NAME"] ?? throw new InvalidOperationException("AZURE_OPENAI_DEPLOYMENT_NAME is not set.");

 // Create the AI agent
-// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
-// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
-// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
 AzureOpenAIClient azureOpenAIClient = new(
    new Uri(endpoint),
    new DefaultAzureCredential());
@@ -36,7 +36,7 @@ var pirateAgentBuilder = builder.AddAIAgent(
    chatClientServiceKey: "chat-model")
    .WithAITool(new CustomAITool())
    .WithAITool(new CustomFunctionTool())
-    .WithInMemorySessionStore();
+    .WithInMemoryThreadStore();

 var knightsKnavesAgentBuilder = builder.AddAIAgent("knights-and-knaves", (sp, key) =>
 {
@@ -70,7 +70,7 @@ var knightsKnavesAgentBuilder = builder.AddAIAgent("knights-and-knaves", (sp, ke
        If the user asks a general question about their surrounding, make something up which is consistent with the scenario.
        """, "Narrator");

-    return AgentWorkflowBuilder.BuildConcurrent([knight, knave, narrator]).AsAIAgent(name: key);
+    return AgentWorkflowBuilder.BuildConcurrent([knight, knave, narrator]).AsAgent(name: key);
 });

 // Workflow consisting of multiple specialized agents
@@ -28,13 +28,13 @@ internal sealed class A2AAgentClient : AgentClientBase
    public override async IAsyncEnumerable<AgentResponseUpdate> RunStreamingAsync(
        string agentName,
        IList<ChatMessage> messages,
-        string? sessionId = null,
+        string? threadId = null,
        [EnumeratorCancellation] CancellationToken cancellationToken = default)
    {
        this._logger.LogInformation("Running agent {AgentName} with {MessageCount} messages via A2A", agentName, messages.Count);

        var (a2aClient, _) = this.ResolveClient(agentName);
-        var contextId = sessionId ?? Guid.NewGuid().ToString("N");
+        var contextId = threadId ?? Guid.NewGuid().ToString("N");

        // Convert and send messages via A2A without try-catch in yield method
        var results = new List<AgentResponseUpdate>();
@@ -16,13 +16,13 @@ internal abstract class AgentClientBase
    /// </summary>
    /// <param name="agentName">The name of the agent to run.</param>
    /// <param name="messages">The messages to send to the agent.</param>
-    /// <param name="sessionId">Optional session identifier for conversation continuity.</param>
+    /// <param name="threadId">Optional thread identifier for conversation continuity.</param>
    /// <param name="cancellationToken">Cancellation token.</param>
    /// <returns>An asynchronous enumerable of agent response updates.</returns>
    public abstract IAsyncEnumerable<AgentResponseUpdate> RunStreamingAsync(
        string agentName,
        IList<ChatMessage> messages,
-        string? sessionId = null,
+        string? threadId = null,
        CancellationToken cancellationToken = default);

    /// <summary>
@@ -34,3 +34,16 @@ internal abstract class AgentClientBase
    public virtual Task<AgentCard?> GetAgentCardAsync(string agentName, CancellationToken cancellationToken = default)
        => Task.FromResult<AgentCard?>(null);
 }
+
+/// <summary>
+/// Helper class to create a thread-like wrapper for agent clients.
+/// </summary>
+public class AgentClientThread
+{
+    public string ThreadId { get; }
+
+    public AgentClientThread(string? threadId = null)
+    {
+        this.ThreadId = threadId ?? Guid.NewGuid().ToString("N");
+    }
+}
@@ -19,7 +19,7 @@ internal sealed class OpenAIChatCompletionsAgentClient(HttpClient httpClient) :
    public override async IAsyncEnumerable<AgentResponseUpdate> RunStreamingAsync(
        string agentName,
        IList<ChatMessage> messages,
-        string? sessionId = null,
+        string? threadId = null,
        [EnumeratorCancellation] CancellationToken cancellationToken = default)
    {
        OpenAIClientOptions options = new()
@@ -18,7 +18,7 @@ internal sealed class OpenAIResponsesAgentClient(HttpClient httpClient) : AgentC
    public override async IAsyncEnumerable<AgentResponseUpdate> RunStreamingAsync(
        string agentName,
        IList<ChatMessage> messages,
-        string? sessionId = null,
+        string? threadId = null,
        [EnumeratorCancellation] CancellationToken cancellationToken = default)
    {
        OpenAIClientOptions options = new()
@@ -30,7 +30,7 @@ internal sealed class OpenAIResponsesAgentClient(HttpClient httpClient) : AgentC
        var openAiClient = new ResponsesClient(model: agentName, credential: new ApiKeyCredential("dummy-key"), options: options).AsIChatClient();
        var chatOptions = new ChatOptions()
        {
-            ConversationId = sessionId
+            ConversationId = threadId
        };

        await foreach (var update in openAiClient.GetStreamingResponseAsync(messages, chatOptions, cancellationToken: cancellationToken))
@@ -36,7 +36,7 @@
  </ItemGroup>
  -->
  <ItemGroup>
-    <ProjectReference Include="..\..\..\..\..\src\Microsoft.Agents.AI.Hosting.AzureFunctions\Microsoft.Agents.AI.Hosting.AzureFunctions.csproj" />
-    <ProjectReference Include="..\..\..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
+    <ProjectReference Include="..\..\..\src\Microsoft.Agents.AI.Hosting.AzureFunctions\Microsoft.Agents.AI.Hosting.AzureFunctions.csproj" />
+    <ProjectReference Include="..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
  </ItemGroup>
 </Project>
@@ -1,7 +1,5 @@
 // Copyright (c) Microsoft. All rights reserved.

-#pragma warning disable IDE0002 // Simplify Member Access
-
 using Azure;
 using Azure.AI.OpenAI;
 using Azure.Identity;
@@ -19,12 +17,9 @@ string deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYM

 // Use Azure Key Credential if provided, otherwise use Azure CLI Credential.
 string? azureOpenAiKey = System.Environment.GetEnvironmentVariable("AZURE_OPENAI_KEY");
-// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
-// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
-// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
 AzureOpenAIClient client = !string.IsNullOrEmpty(azureOpenAiKey)
    ? new AzureOpenAIClient(new Uri(endpoint), new AzureKeyCredential(azureOpenAiKey))
-    : new AzureOpenAIClient(new Uri(endpoint), new DefaultAzureCredential());
+    : new AzureOpenAIClient(new Uri(endpoint), new AzureCliCredential());

 // Set up an AI agent following the standard Microsoft Agent Framework pattern.
 const string JokerName = "Joker";
@@ -7,4 +7,4 @@
    "AZURE_OPENAI_ENDPOINT": "<AZURE_OPENAI_ENDPOINT>",
    "AZURE_OPENAI_DEPLOYMENT": "<AZURE_OPENAI_DEPLOYMENT>"
  }
-}
+}
@@ -36,7 +36,7 @@
  </ItemGroup>
  -->
  <ItemGroup>
-    <ProjectReference Include="..\..\..\..\..\src\Microsoft.Agents.AI.Hosting.AzureFunctions\Microsoft.Agents.AI.Hosting.AzureFunctions.csproj" />
-    <ProjectReference Include="..\..\..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
+    <ProjectReference Include="..\..\..\src\Microsoft.Agents.AI.Hosting.AzureFunctions\Microsoft.Agents.AI.Hosting.AzureFunctions.csproj" />
+    <ProjectReference Include="..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
  </ItemGroup>
 </Project>
@@ -19,15 +19,15 @@ public static class FunctionTriggers
    public static async Task<string> RunOrchestrationAsync([OrchestrationTrigger] TaskOrchestrationContext context)
    {
        DurableAIAgent writer = context.GetAgent("WriterAgent");
-        AgentSession writerSession = await writer.CreateSessionAsync();
+        AgentThread writerThread = await writer.GetNewThreadAsync();

        AgentResponse<TextResponse> initial = await writer.RunAsync<TextResponse>(
            message: "Write a concise inspirational sentence about learning.",
-            session: writerSession);
+            thread: writerThread);

        AgentResponse<TextResponse> refined = await writer.RunAsync<TextResponse>(
            message: $"Improve this further while keeping it under 25 words: {initial.Result.Text}",
-            session: writerSession);
+            thread: writerThread);

        return refined.Result.Text;
    }
@@ -1,7 +1,5 @@
 // Copyright (c) Microsoft. All rights reserved.

-#pragma warning disable IDE0002 // Simplify Member Access
-
 using Azure;
 using Azure.AI.OpenAI;
 using Azure.Identity;
@@ -19,14 +17,11 @@ string deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYM

 // Use Azure Key Credential if provided, otherwise use Azure CLI Credential.
 string? azureOpenAiKey = System.Environment.GetEnvironmentVariable("AZURE_OPENAI_KEY");
-// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
-// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
-// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
 AzureOpenAIClient client = !string.IsNullOrEmpty(azureOpenAiKey)
    ? new AzureOpenAIClient(new Uri(endpoint), new AzureKeyCredential(azureOpenAiKey))
-    : new AzureOpenAIClient(new Uri(endpoint), new DefaultAzureCredential());
+    : new AzureOpenAIClient(new Uri(endpoint), new AzureCliCredential());

-// Single agent used by the orchestration to demonstrate sequential calls on the same session.
+// Single agent used by the orchestration to demonstrate sequential calls on the same thread.
 const string WriterName = "WriterAgent";
 const string WriterInstructions =
    """
@@ -1,11 +1,11 @@
 # Single Agent Orchestration Sample

-This sample demonstrates how to use the Durable Agent Framework (DAFx) to create a simple Azure Functions app that orchestrates sequential calls to a single AI agent using the same session for context continuity.
+This sample demonstrates how to use the Durable Agent Framework (DAFx) to create a simple Azure Functions app that orchestrates sequential calls to a single AI agent using the same conversation thread for context continuity.

 ## Key Concepts Demonstrated

 - Orchestrating multiple interactions with the same agent in a deterministic order
- Using the same `AgentSession` across multiple calls to maintain conversational context
+- Using the same `AgentThread` across multiple calls to maintain conversational context
 - Durable orchestration with automatic checkpointing and resumption from failures
 - HTTP API integration for starting and monitoring orchestrations

@@ -36,7 +36,7 @@
  </ItemGroup>
  -->
  <ItemGroup>
-    <ProjectReference Include="..\..\..\..\..\src\Microsoft.Agents.AI.Hosting.AzureFunctions\Microsoft.Agents.AI.Hosting.AzureFunctions.csproj" />
-    <ProjectReference Include="..\..\..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
+    <ProjectReference Include="..\..\..\src\Microsoft.Agents.AI.Hosting.AzureFunctions\Microsoft.Agents.AI.Hosting.AzureFunctions.csproj" />
+    <ProjectReference Include="..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
  </ItemGroup>
 </Project>
@@ -1,7 +1,5 @@
 // Copyright (c) Microsoft. All rights reserved.

-#pragma warning disable IDE0002 // Simplify Member Access
-
 using Azure;
 using Azure.AI.OpenAI;
 using Azure.Identity;
@@ -19,12 +17,9 @@ string deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYM

 // Use Azure Key Credential if provided, otherwise use Azure CLI Credential.
 string? azureOpenAiKey = System.Environment.GetEnvironmentVariable("AZURE_OPENAI_KEY");
-// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
-// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
-// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
 AzureOpenAIClient client = !string.IsNullOrEmpty(azureOpenAiKey)
    ? new AzureOpenAIClient(new Uri(endpoint), new AzureKeyCredential(azureOpenAiKey))
-    : new AzureOpenAIClient(new Uri(endpoint), new DefaultAzureCredential());
+    : new AzureOpenAIClient(new Uri(endpoint), new AzureCliCredential());

 // Two agents used by the orchestration to demonstrate concurrent execution.
 const string PhysicistName = "PhysicistAgent";
@@ -36,7 +36,7 @@
  </ItemGroup>
  -->
  <ItemGroup>
-    <ProjectReference Include="..\..\..\..\..\src\Microsoft.Agents.AI.Hosting.AzureFunctions\Microsoft.Agents.AI.Hosting.AzureFunctions.csproj" />
-    <ProjectReference Include="..\..\..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
+    <ProjectReference Include="..\..\..\src\Microsoft.Agents.AI.Hosting.AzureFunctions\Microsoft.Agents.AI.Hosting.AzureFunctions.csproj" />
+    <ProjectReference Include="..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
  </ItemGroup>
 </Project>
@@ -21,7 +21,7 @@ public static class FunctionTriggers

        // Get the spam detection agent
        DurableAIAgent spamDetectionAgent = context.GetAgent("SpamDetectionAgent");
-        AgentSession spamSession = await spamDetectionAgent.CreateSessionAsync();
+        AgentThread spamThread = await spamDetectionAgent.GetNewThreadAsync();

        // Step 1: Check if the email is spam
        AgentResponse<DetectionResult> spamDetectionResponse = await spamDetectionAgent.RunAsync<DetectionResult>(
@@ -31,7 +31,7 @@ public static class FunctionTriggers
                Email ID: {email.EmailId}
                Content: {email.EmailContent}
                """,
-            session: spamSession);
+            thread: spamThread);
        DetectionResult result = spamDetectionResponse.Result;

        // Step 2: Conditional logic based on spam detection result
@@ -43,7 +43,7 @@ public static class FunctionTriggers

        // Generate and send response for legitimate email
        DurableAIAgent emailAssistantAgent = context.GetAgent("EmailAssistantAgent");
-        AgentSession emailSession = await emailAssistantAgent.CreateSessionAsync();
+        AgentThread emailThread = await emailAssistantAgent.GetNewThreadAsync();

        AgentResponse<EmailResponse> emailAssistantResponse = await emailAssistantAgent.RunAsync<EmailResponse>(
            message:
@@ -53,7 +53,7 @@ public static class FunctionTriggers
                    Email ID: {email.EmailId}
                    Content: {email.EmailContent}
                    """,
-            session: emailSession);
+            thread: emailThread);

        EmailResponse emailResponse = emailAssistantResponse.Result;

@@ -1,7 +1,5 @@
 // Copyright (c) Microsoft. All rights reserved.

-#pragma warning disable IDE0002 // Simplify Member Access
-
 using Azure;
 using Azure.AI.OpenAI;
 using Azure.Identity;
@@ -19,12 +17,9 @@ string deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYM

 // Use Azure Key Credential if provided, otherwise use Azure CLI Credential.
 string? azureOpenAiKey = System.Environment.GetEnvironmentVariable("AZURE_OPENAI_KEY");
-// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
-// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
-// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
 AzureOpenAIClient client = !string.IsNullOrEmpty(azureOpenAiKey)
    ? new AzureOpenAIClient(new Uri(endpoint), new AzureKeyCredential(azureOpenAiKey))
-    : new AzureOpenAIClient(new Uri(endpoint), new DefaultAzureCredential());
+    : new AzureOpenAIClient(new Uri(endpoint), new AzureCliCredential());

 // Two agents used by the orchestration to demonstrate conditional logic.
 const string SpamDetectionName = "SpamDetectionAgent";
@@ -37,7 +37,7 @@
  </ItemGroup>
  -->
  <ItemGroup>
-    <ProjectReference Include="..\..\..\..\..\src\Microsoft.Agents.AI.Hosting.AzureFunctions\Microsoft.Agents.AI.Hosting.AzureFunctions.csproj" />
-    <ProjectReference Include="..\..\..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
+    <ProjectReference Include="..\..\..\src\Microsoft.Agents.AI.Hosting.AzureFunctions\Microsoft.Agents.AI.Hosting.AzureFunctions.csproj" />
+    <ProjectReference Include="..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
  </ItemGroup>
 </Project>
@@ -24,7 +24,7 @@ public static class FunctionTriggers

        // Get the writer agent
        DurableAIAgent writerAgent = context.GetAgent("WriterAgent");
-        AgentSession writerSession = await writerAgent.CreateSessionAsync();
+        AgentThread writerThread = await writerAgent.GetNewThreadAsync();

        // Set initial status
        context.SetCustomStatus($"Starting content generation for topic: {input.Topic}");
@@ -32,7 +32,7 @@ public static class FunctionTriggers
        // Step 1: Generate initial content
        AgentResponse<GeneratedContent> writerResponse = await writerAgent.RunAsync<GeneratedContent>(
            message: $"Write a short article about '{input.Topic}'.",
-            session: writerSession);
+            thread: writerThread);
        GeneratedContent content = writerResponse.Result;

        // Human-in-the-loop iteration - we set a maximum number of attempts to avoid infinite loops
@@ -81,7 +81,7 @@ public static class FunctionTriggers
                    
                    Human Feedback: {humanResponse.Feedback}
                    """,
-                session: writerSession);
+                thread: writerThread);

            content = writerResponse.Result;
        }
@@ -1,7 +1,5 @@
 // Copyright (c) Microsoft. All rights reserved.

-#pragma warning disable IDE0002 // Simplify Member Access
-
 using Azure;
 using Azure.AI.OpenAI;
 using Azure.Identity;
@@ -19,12 +17,9 @@ string deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYM

 // Use Azure Key Credential if provided, otherwise use Azure CLI Credential.
 string? azureOpenAiKey = System.Environment.GetEnvironmentVariable("AZURE_OPENAI_KEY");
-// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
-// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
-// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
 AzureOpenAIClient client = !string.IsNullOrEmpty(azureOpenAiKey)
    ? new AzureOpenAIClient(new Uri(endpoint), new AzureKeyCredential(azureOpenAiKey))
-    : new AzureOpenAIClient(new Uri(endpoint), new DefaultAzureCredential());
+    : new AzureOpenAIClient(new Uri(endpoint), new AzureCliCredential());

 // Single agent used by the orchestration to demonstrate human-in-the-loop workflow.
 const string WriterName = "WriterAgent";
@@ -36,7 +36,7 @@
  </ItemGroup>
  -->
  <ItemGroup>
-    <ProjectReference Include="..\..\..\..\..\src\Microsoft.Agents.AI.Hosting.AzureFunctions\Microsoft.Agents.AI.Hosting.AzureFunctions.csproj" />
-    <ProjectReference Include="..\..\..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
+    <ProjectReference Include="..\..\..\src\Microsoft.Agents.AI.Hosting.AzureFunctions\Microsoft.Agents.AI.Hosting.AzureFunctions.csproj" />
+    <ProjectReference Include="..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
  </ItemGroup>
 </Project>
@@ -20,7 +20,7 @@ public static class FunctionTriggers

        // Get the writer agent
        DurableAIAgent writerAgent = context.GetAgent("Writer");
-        AgentSession writerSession = await writerAgent.CreateSessionAsync();
+        AgentThread writerThread = await writerAgent.GetNewThreadAsync();

        // Set initial status
        context.SetCustomStatus($"Starting content generation for topic: {input.Topic}");
@@ -28,7 +28,7 @@ public static class FunctionTriggers
        // Step 1: Generate initial content
        AgentResponse<GeneratedContent> writerResponse = await writerAgent.RunAsync<GeneratedContent>(
            message: $"Write a short article about '{input.Topic}'.",
-            session: writerSession);
+            thread: writerThread);
        GeneratedContent content = writerResponse.Result;

        // Human-in-the-loop iteration - we set a maximum number of attempts to avoid infinite loops
@@ -102,7 +102,7 @@ public static class FunctionTriggers
                    
                    Human Feedback: {humanResponse.Feedback}
                    """,
-                session: writerSession);
+                thread: writerThread);

            content = writerResponse.Result;
        }
@@ -1,7 +1,5 @@
 // Copyright (c) Microsoft. All rights reserved.

-#pragma warning disable IDE0002 // Simplify Member Access
-
 using Azure;
 using Azure.AI.OpenAI;
 using Azure.Identity;
@@ -23,12 +21,9 @@ string deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYM

 // Use Azure Key Credential if provided, otherwise use Azure CLI Credential.
 string? azureOpenAiKey = System.Environment.GetEnvironmentVariable("AZURE_OPENAI_KEY");
-// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
-// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
-// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
 AzureOpenAIClient client = !string.IsNullOrEmpty(azureOpenAiKey)
    ? new AzureOpenAIClient(new Uri(endpoint), new AzureKeyCredential(azureOpenAiKey))
-    : new AzureOpenAIClient(new Uri(endpoint), new DefaultAzureCredential());
+    : new AzureOpenAIClient(new Uri(endpoint), new AzureCliCredential());

 // Agent used by the orchestration to write content.
 const string WriterAgentName = "Writer";
@@ -47,7 +47,7 @@ internal sealed class Tools(ILogger<Tools> logger)
    {
        this._logger.LogInformation("Getting status for workflow instance: {InstanceId}", instanceId);

-        // Get the current agent context using the session-static property
+        // Get the current agent context using the thread-static property
        OrchestrationMetadata? status = await DurableAgentContext.Current.GetOrchestrationStatusAsync(
            instanceId,
            includeDetails);
@@ -1,4 +1,4 @@
-<Project Sdk="Microsoft.NET.Sdk">
+<Project Sdk="Microsoft.NET.Sdk">
  <PropertyGroup>
    <TargetFrameworks>net10.0</TargetFrameworks>
    <AzureFunctionsVersion>v4</AzureFunctionsVersion>
@@ -36,7 +36,7 @@
  </ItemGroup>
  -->
  <ItemGroup>
-    <ProjectReference Include="..\..\..\..\..\src\Microsoft.Agents.AI.Hosting.AzureFunctions\Microsoft.Agents.AI.Hosting.AzureFunctions.csproj" />
-    <ProjectReference Include="..\..\..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
+    <ProjectReference Include="..\..\..\src\Microsoft.Agents.AI.Hosting.AzureFunctions\Microsoft.Agents.AI.Hosting.AzureFunctions.csproj" />
+    <ProjectReference Include="..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
  </ItemGroup>
 </Project>
@@ -5,8 +5,6 @@
 // generate a remote MCP endpoint for the app at /runtime/webhooks/mcp with a agent-specific
 // query tool name.

-#pragma warning disable IDE0002 // Simplify Member Access
-
 using Azure;
 using Azure.AI.OpenAI;
 using Azure.Identity;
@@ -25,12 +23,9 @@ string deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYM

 // Use Azure Key Credential if provided, otherwise use Azure CLI Credential.
 string? azureOpenAiKey = System.Environment.GetEnvironmentVariable("AZURE_OPENAI_KEY");
-// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
-// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
-// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
 AzureOpenAIClient client = !string.IsNullOrEmpty(azureOpenAiKey)
    ? new AzureOpenAIClient(new Uri(endpoint), new AzureKeyCredential(azureOpenAiKey))
-    : new AzureOpenAIClient(new Uri(endpoint), new DefaultAzureCredential());
+    : new AzureOpenAIClient(new Uri(endpoint), new AzureCliCredential());

 // Define three AI agents we are going to use in this application.
 AIAgent agent1 = client.GetChatClient(deploymentName).AsAIAgent("You are good at telling jokes.", "Joker");
@@ -41,7 +41,7 @@
  </ItemGroup>
  -->
  <ItemGroup>
-    <ProjectReference Include="..\..\..\..\..\src\Microsoft.Agents.AI.Hosting.AzureFunctions\Microsoft.Agents.AI.Hosting.AzureFunctions.csproj" />
-    <ProjectReference Include="..\..\..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
+    <ProjectReference Include="..\..\..\src\Microsoft.Agents.AI.Hosting.AzureFunctions\Microsoft.Agents.AI.Hosting.AzureFunctions.csproj" />
+    <ProjectReference Include="..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
  </ItemGroup>
 </Project>
@@ -94,15 +94,15 @@ public sealed class FunctionTriggers

        AIAgent agentProxy = durableClient.AsDurableAgentProxy(context, "TravelPlanner");

-        // Create a new agent session
-        AgentSession session = await agentProxy.CreateSessionAsync(cancellationToken);
-        string agentSessionId = session.GetService<AgentSessionId>().ToString();
+        // Create a new agent thread
+        AgentThread thread = await agentProxy.GetNewThreadAsync(cancellationToken);
+        string agentSessionId = thread.GetService<AgentSessionId>().ToString();

        this._logger.LogInformation("Creating new agent session: {AgentSessionId}", agentSessionId);

        // Run the agent in the background (fire-and-forget)
        DurableAgentRunOptions options = new() { IsFireAndForget = true };
-        await agentProxy.RunAsync(prompt, session, options, cancellationToken);
+        await agentProxy.RunAsync(prompt, thread, options, cancellationToken);

        this._logger.LogInformation("Agent run started for session: {AgentSessionId}", agentSessionId);

@@ -8,8 +8,6 @@
 // This pattern is inspired by OpenAI's background mode for the Responses API, which allows clients
 // to disconnect and reconnect to ongoing agent responses without losing messages.

-#pragma warning disable IDE0002 // Simplify Member Access
-
 using Azure;
 using Azure.AI.OpenAI;
 using Azure.Identity;
@@ -40,12 +38,9 @@ int redisStreamTtlMinutes = int.TryParse(

 // Use Azure Key Credential if provided, otherwise use Azure CLI Credential.
 string? azureOpenAiKey = System.Environment.GetEnvironmentVariable("AZURE_OPENAI_KEY");
-// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
-// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
-// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
 AzureOpenAIClient client = !string.IsNullOrEmpty(azureOpenAiKey)
    ? new AzureOpenAIClient(new Uri(endpoint), new AzureKeyCredential(azureOpenAiKey))
-    : new AzureOpenAIClient(new Uri(endpoint), new DefaultAzureCredential());
+    : new AzureOpenAIClient(new Uri(endpoint), new AzureCliCredential());

 // Travel Planner agent instructions - designed to produce longer responses for demonstrating streaming.
 const string TravelPlannerName = "TravelPlanner";
--- a/Show More
+++ b/Show More