agent-framework/python/samples/02-agents/harness

Harness Agent Samples

This folder demonstrates create_harness_agent — a factory function that builds a pre-configured, batteries-included agent by assembling the full agent pipeline from a chat client.

What is create_harness_agent?

create_harness_agent bundles the following features into a single Agent instance:

Feature	Description
Function invocation	Automatic tool calling loop
Per-service-call persistence	History persisted after every model call
Compaction	Context-window management (sliding window + tool result compaction)
TodoProvider	Todo list management for planning and tracking
AgentModeProvider	Plan/execute mode tracking
MemoryContextProvider	File-based durable memory (when memory_store provided)
SkillsProvider	File-based skill discovery and progressive loading
OpenTelemetry	Built-in observability

Each feature can be disabled or customized via keyword arguments.

Samples

File	Description
harness_research.py	Interactive research assistant with web search and planning workflow

Running

# Set your Foundry environment variables
export FOUNDRY_PROJECT_ENDPOINT="https://your-project.services.ai.azure.com/api/projects/your-project-name"
export FOUNDRY_MODEL="your-model-deployment-name"

# Authenticate with Azure (required for AzureCliCredential)
az login

# Run the research sample
python samples/02-agents/harness/harness_research.py

Key Concepts

Minimal Setup

create_harness_agent requires only a chat client and token budget parameters:

from agent_framework import create_harness_agent
from agent_framework.foundry import FoundryChatClient
from azure.identity import AzureCliCredential

agent = create_harness_agent(
    client=FoundryChatClient(credential=AzureCliCredential()),
    max_context_window_tokens=128_000,
    max_output_tokens=16_384,
)

Customization

Disable or customize any feature:

agent = create_harness_agent(
    client=client,
    max_context_window_tokens=128_000,
    max_output_tokens=16_384,
    name="my-agent",
    agent_instructions="Custom instructions here.",
    disable_todo=True,          # Skip todo management
    disable_mode=True,          # Skip plan/execute modes
    disable_compaction=True,    # Skip compaction
)

Plan/Execute Workflow

The AgentModeProvider enables a two-phase workflow:

1. Plan mode — Interactive: the agent asks questions, creates todos, gets approval
2. Execute mode — Autonomous: the agent works through todos independently