chore: import upstream snapshot with attribution

python/samples/03-workflows/declarative/marketing/README.md

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+# Marketing Copy Workflow
+
+This sample demonstrates a sequential multi-agent pipeline for generating marketing copy from a product description.
+
+## Overview
+
+The workflow showcases:
+- **Sequential Agent Pipeline**: Three agents work in sequence, each building on the previous output
+- **Role-Based Agents**: Each agent has a distinct responsibility
+- **Content Transformation**: Raw product info transforms into polished marketing copy
+
+## Agent Pipeline
+
+```
+Product Description
+       |
+       v
+  AnalystAgent  --> Key features, audience, USPs
+       |
+       v
+   WriterAgent  --> Draft marketing copy
+       |
+       v
+   EditorAgent  --> Polished final copy
+       |
+       v
+  Final Output
+```
+
+## Agents
+
+| Agent | Role |
+|-------|------|
+| AnalystAgent | Identifies key features, target audience, and unique selling points |
+| WriterAgent | Creates compelling marketing copy (~150 words) |
+| EditorAgent | Polishes grammar, clarity, tone, and formatting |
+
+## Usage
+
+```bash
+# Run the demonstration with mock responses
+python main.py
+```
+
+## Example Input
+
+```
+An eco-friendly stainless steel water bottle that keeps drinks cold for 24 hours.
+```
+
+## Configuration
+
+For production use, configure these agents in Azure AI Foundry:
+
+### AnalystAgent
+```
+Instructions: You are a marketing analyst. Given a product description, identify:
+- Key features
+- Target audience
+- Unique selling points
+```
+
+### WriterAgent
+```
+Instructions: You are a marketing copywriter. Given a block of text describing
+features, audience, and USPs, compose a compelling marketing copy (like a
+newsletter section) that highlights these points. Output should be short
+(around 150 words), output just the copy as a single text block.
+```
+
+### EditorAgent
+```
+Instructions: You are an editor. Given the draft copy, correct grammar,
+improve clarity, ensure consistent tone, give format and make it polished.
+Output the final improved copy as a single text block.
+```

python/samples/03-workflows/declarative/marketing/main.py

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+# Copyright (c) Microsoft. All rights reserved.
+
+"""
+Run the marketing copy workflow sample.
+
+Usage:
+    python main.py
+
+Demonstrates sequential multi-agent pipeline:
+- AnalystAgent: Identifies key features, target audience, USPs
+- WriterAgent: Creates compelling marketing copy
+- EditorAgent: Polishes grammar, clarity, and tone
+"""
+
+import asyncio
+import os
+from pathlib import Path
+
+from agent_framework import Agent
+from agent_framework.declarative import WorkflowFactory
+from agent_framework.foundry import FoundryChatClient
+from azure.identity import AzureCliCredential
+
+# Copyright (c) Microsoft. All rights reserved.
+
+
+ANALYST_INSTRUCTIONS = """You are a product analyst. Analyze the given product and identify:
+1. Key features and benefits
+2. Target audience demographics
+3. Unique selling propositions (USPs)
+4. Competitive advantages
+
+Be concise and structured in your analysis."""
+
+WRITER_INSTRUCTIONS = """You are a marketing copywriter. Based on the product analysis provided,
+create compelling marketing copy that:
+1. Has a catchy headline
+2. Highlights key benefits
+3. Speaks to the target audience
+4. Creates emotional connection
+5. Includes a call to action
+
+Write in an engaging, persuasive tone."""
+
+EDITOR_INSTRUCTIONS = """You are a senior editor. Review and polish the marketing copy:
+1. Fix any grammar or spelling issues
+2. Improve clarity and flow
+3. Ensure consistent tone
+4. Tighten the prose
+5. Make it more impactful
+
+Return the final polished version."""
+
+
+async def main() -> None:
+    """Run the marketing workflow with real Azure AI agents."""
+    client = FoundryChatClient(
+        project_endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
+        model=os.environ["FOUNDRY_MODEL"],
+        credential=AzureCliCredential(),
+    )
+
+    analyst_agent = Agent(
+        client=client,
+        name="AnalystAgent",
+        instructions=ANALYST_INSTRUCTIONS,
+    )
+    writer_agent = Agent(
+        client=client,
+        name="WriterAgent",
+        instructions=WRITER_INSTRUCTIONS,
+    )
+    editor_agent = Agent(
+        client=client,
+        name="EditorAgent",
+        instructions=EDITOR_INSTRUCTIONS,
+    )
+
+    factory = WorkflowFactory(
+        agents={
+            "AnalystAgent": analyst_agent,
+            "WriterAgent": writer_agent,
+            "EditorAgent": editor_agent,
+        }
+    )
+
+    workflow_path = Path(__file__).parent / "workflow.yaml"
+    workflow = factory.create_workflow_from_yaml_path(workflow_path)
+
+    print(f"Loaded workflow: {workflow.name}")
+    print("=" * 60)
+    print("Marketing Copy Generation Pipeline")
+    print("=" * 60)
+
+    # Pass a simple string input - like .NET
+    product = "An eco-friendly stainless steel water bottle that keeps drinks cold for 24 hours."
+
+    async for event in workflow.run(product, stream=True):
+        if event.type == "output":
+            print(f"{event.data}", end="", flush=True)
+
+    print("\n" + "=" * 60)
+    print("Pipeline Complete")
+    print("=" * 60)
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

python/samples/03-workflows/declarative/marketing/workflow.yaml

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+#
+# This workflow demonstrates sequential agent interaction to develop product marketing copy.
+#
+# Example input:
+# An eco-friendly stainless steel water bottle that keeps drinks cold for 24 hours.
+#
+kind: Workflow
+trigger:
+
+  kind: OnConversationStart
+  id: workflow_demo
+  actions:
+
+    - kind: InvokeAzureAgent
+      id: invoke_analyst
+      conversationId: =System.ConversationId
+      agent:
+        name: AnalystAgent
+
+    - kind: InvokeAzureAgent
+      id: invoke_writer
+      conversationId: =System.ConversationId
+      agent:
+        name: WriterAgent
+
+    - kind: InvokeAzureAgent
+      id: invoke_editor
+      conversationId: =System.ConversationId
+      agent:
+        name: EditorAgent