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Python: restructure: Python samples into progressive 01-05 layout (#3862)
* restructure: Python samples into progressive 01-05 layout - 01-get-started/: 6 numbered steps (hello agent → hosting) - 02-agents/: all agent concept samples (tools, middleware, providers, etc.) - 03-workflows/: ALL existing workflow samples preserved as-is - 04-hosting/: azure-functions, durabletask, a2a - 05-end-to-end/: demos, evaluation, hosted agents - Old files moved to _to_delete/ for review - Added AGENTS.md with structure documentation - autogen-migration/ and semantic-kernel-migration/ preserved at root * fix: switch to AzureOpenAI Foundry, fix CI failures - Switch all 01-get-started samples to AzureOpenAIResponsesClient with Azure AI Foundry project endpoint (AZURE_AI_PROJECT_ENDPOINT + AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME + AzureCliCredential) - Add _to_delete/ and 05-end-to-end/ to pyrightconfig.samples.json excludes - Fix test paths in packages/ that referenced old getting_started/ dirs: durabletask conftest + streaming test, azurefunctions conftest, devui conftest + capture_messages + openai_sdk_integration - Fix workflow_as_agent_human_in_the_loop.py import (sibling import) - Update hosting READMEs and tool comment paths - Replace root README.md with new structure overview - Update AGENTS.md to document Azure OpenAI Foundry as default provider * cleanup: remove _to_delete folder, copy resource files to active dirs All files in _to_delete/ were either: - Exact duplicates of files in the new structure (240 files) - Same file with only comment path updates (100 files) - One import-fix diff (workflow_as_agent_human_in_the_loop.py) - One superseded minimal_sample.py Resource files (sample.pdf, countries.json, employees.pdf, weather.json) copied to 02-agents/sample_assets/ and 02-agents/resources/ since active samples reference them. * fix: address PR review comments, centralize resources, remove root duplicates - Fix type annotation in 04_memory.py (string union -> proper types) - Fix old sample paths in observability files - Fix grammar/spelling in observability samples - Move sample_assets/ and resources/ to shared/ folder - Remove 8 duplicate observability files from 02-agents root - Update resource path references in multimodal_input and provider samples * fix: update broken links from old getting_started paths to new structure - Update relative paths in READMEs: getting_started/ → 01-get-started/, 02-agents/, 03-workflows/, 04-hosting/, 05-end-to-end/ - Fix absolute GitHub URLs in package READMEs - Fix broken link in ollama package README * fix: convert absolute GitHub URLs to relative paths for link checker Absolute URLs to python/samples/ on main branch 404 until PR merges. Converted to relative paths that linkspector can verify locally. * fix: update link for handoff sample moved to orchestrations/ * fix: update chatkit-integration README path from demos/ to 05-end-to-end/ * fix: update broken links in orchestrations README to match flat directory structure
This commit is contained in:
Eduard van Valkenburg
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# Marketing Copy Workflow
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This sample demonstrates a sequential multi-agent pipeline for generating marketing copy from a product description.
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## Overview
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The workflow showcases:
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- **Sequential Agent Pipeline**: Three agents work in sequence, each building on the previous output
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- **Role-Based Agents**: Each agent has a distinct responsibility
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- **Content Transformation**: Raw product info transforms into polished marketing copy
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## Agent Pipeline
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```
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Product Description
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v
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AnalystAgent --> Key features, audience, USPs
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v
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WriterAgent --> Draft marketing copy
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v
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EditorAgent --> Polished final copy
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v
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Final Output
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```
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## Agents
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| Agent | Role |
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|-------|------|
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| AnalystAgent | Identifies key features, target audience, and unique selling points |
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| WriterAgent | Creates compelling marketing copy (~150 words) |
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| EditorAgent | Polishes grammar, clarity, tone, and formatting |
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## Usage
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```bash
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# Run the demonstration with mock responses
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python main.py
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```
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## Example Input
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```
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An eco-friendly stainless steel water bottle that keeps drinks cold for 24 hours.
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```
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## Configuration
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For production use, configure these agents in Azure AI Foundry:
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### AnalystAgent
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```
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Instructions: You are a marketing analyst. Given a product description, identify:
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- Key features
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- Target audience
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- Unique selling points
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```
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### WriterAgent
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```
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Instructions: You are a marketing copywriter. Given a block of text describing
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features, audience, and USPs, compose a compelling marketing copy (like a
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newsletter section) that highlights these points. Output should be short
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(around 150 words), output just the copy as a single text block.
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```
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### EditorAgent
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```
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Instructions: You are an editor. Given the draft copy, correct grammar,
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improve clarity, ensure consistent tone, give format and make it polished.
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Output the final improved copy as a single text block.
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```
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# Copyright (c) Microsoft. All rights reserved.
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"""
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Run the marketing copy workflow sample.
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Usage:
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python main.py
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Demonstrates sequential multi-agent pipeline:
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- AnalystAgent: Identifies key features, target audience, USPs
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- WriterAgent: Creates compelling marketing copy
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- EditorAgent: Polishes grammar, clarity, and tone
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"""
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import asyncio
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import os
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from pathlib import Path
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from agent_framework.azure import AzureOpenAIResponsesClient
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from agent_framework.declarative import WorkflowFactory
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from azure.identity import AzureCliCredential
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ANALYST_INSTRUCTIONS = """You are a product analyst. Analyze the given product and identify:
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1. Key features and benefits
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2. Target audience demographics
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3. Unique selling propositions (USPs)
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4. Competitive advantages
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Be concise and structured in your analysis."""
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WRITER_INSTRUCTIONS = """You are a marketing copywriter. Based on the product analysis provided,
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create compelling marketing copy that:
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1. Has a catchy headline
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2. Highlights key benefits
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3. Speaks to the target audience
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4. Creates emotional connection
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5. Includes a call to action
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Write in an engaging, persuasive tone."""
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EDITOR_INSTRUCTIONS = """You are a senior editor. Review and polish the marketing copy:
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1. Fix any grammar or spelling issues
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2. Improve clarity and flow
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3. Ensure consistent tone
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4. Tighten the prose
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5. Make it more impactful
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Return the final polished version."""
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async def main() -> None:
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"""Run the marketing workflow with real Azure AI agents."""
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client = AzureOpenAIResponsesClient(
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project_endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"],
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deployment_name=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
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credential=AzureCliCredential(),
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)
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analyst_agent = client.as_agent(
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name="AnalystAgent",
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instructions=ANALYST_INSTRUCTIONS,
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)
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writer_agent = client.as_agent(
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name="WriterAgent",
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instructions=WRITER_INSTRUCTIONS,
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)
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editor_agent = client.as_agent(
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name="EditorAgent",
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instructions=EDITOR_INSTRUCTIONS,
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)
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factory = WorkflowFactory(
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agents={
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"AnalystAgent": analyst_agent,
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"WriterAgent": writer_agent,
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"EditorAgent": editor_agent,
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}
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)
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workflow_path = Path(__file__).parent / "workflow.yaml"
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workflow = factory.create_workflow_from_yaml_path(workflow_path)
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print(f"Loaded workflow: {workflow.name}")
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print("=" * 60)
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print("Marketing Copy Generation Pipeline")
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print("=" * 60)
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# Pass a simple string input - like .NET
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product = "An eco-friendly stainless steel water bottle that keeps drinks cold for 24 hours."
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async for event in workflow.run(product, stream=True):
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if event.type == "output":
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print(f"{event.data}", end="", flush=True)
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print("\n" + "=" * 60)
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print("Pipeline Complete")
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print("=" * 60)
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if __name__ == "__main__":
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asyncio.run(main())
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#
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# This workflow demonstrates sequential agent interaction to develop product marketing copy.
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#
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# Example input:
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# An eco-friendly stainless steel water bottle that keeps drinks cold for 24 hours.
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#
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kind: Workflow
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trigger:
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kind: OnConversationStart
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id: workflow_demo
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actions:
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- kind: InvokeAzureAgent
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id: invoke_analyst
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conversationId: =System.ConversationId
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agent:
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name: AnalystAgent
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- kind: InvokeAzureAgent
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id: invoke_writer
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conversationId: =System.ConversationId
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agent:
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name: WriterAgent
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- kind: InvokeAzureAgent
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id: invoke_editor
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conversationId: =System.ConversationId
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agent:
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name: EditorAgent
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