Create Agent with Microsoft Agent Framework

Build AI agents, agentic apps, and multi-agent workflows using Microsoft Agent Framework SDK.

Quick Reference

Property	Value
SDK	Microsoft Agent Framework (Python)
Patterns	Single Agent, Multi-Agent Workflow
Server	Azure AI Agent Server SDK (HTTP)
Debug	AI Toolkit Agent Inspector + VSCode
Best For	Enterprise agents with type safety, checkpointing, orchestration

When to Use This Skill

Use when the user wants to:

• Create a new AI agent or agentic application
• Scaffold an agent with tools (MCP, function calling)
• Build multi-agent workflows with orchestration patterns
• Add HTTP server mode to an existing agent
• Configure F5/debug support for VSCode

Defaults

• Language: Python
• SDK: Microsoft Agent Framework (pin version 1.0.0b260107)
• Server: HTTP via Azure AI Agent Server SDK
• Environment: Virtual environment (create or detect existing)

References

Topic	File	Description
Server Pattern	references/agent-as-server.md	HTTP server wrapping (production)
Debug Setup	references/debug-setup.md	VS Code configs for Agent Inspector
Agent Samples	references/agent-samples.md	Single agent, tools, MCP, threads
Workflow Basics	references/workflow-basics.md	Executor types, handler signatures, edges, WorkflowBuilder — start here for any workflow
Workflow Agents	references/workflow-agents.md	Agents as executor nodes, linear pipeline, run_stream event consumption
Workflow Foundry	references/workflow-foundry.md	Foundry agents with bidirectional edges, loop control, register_executor factories

💡 Tip: For advanced patterns (Reflection, Switch-Case, Fan-out/Fan-in, Loop, Human-in-Loop), search microsoft/agent-framework on GitHub.

MCP Tools

This skill delegates to microsoft-foundry MCP tools for model and project operations:

Tool	Purpose
foundry_models_list	Browse model catalog for selection
foundry_models_deployments_list	List deployed models for selection
foundry_resource_get	Get project endpoint

Creation Workflow

1. Gather context (read agent-as-server.md + debug-setup.md + code samples)
2. Select model & configure environment
3. Implement agent/workflow code + HTTP server mode + .vscode/ configs
4. Install dependencies (venv + requirements.txt)
5. Verify startup (Run-Fix loop)
6. Documentation

Step 1: Gather Context

Read reference files based on user's request:

Always read these references:

• Server pattern: agent-as-server.md (required — HTTP server is the default)
• Debug setup: debug-setup.md (required — always generate .vscode/ configs)

Read the relevant code sample:

• Code samples: agent-samples.md, workflow-basics.md, workflow-agents.md, or workflow-foundry.md

Model Selection: Use microsoft-foundry skill's model catalog to help user select and deploy a model.

Recommended: Search microsoft/agent-framework on GitHub for advanced patterns.

Step 2: Select Model & Configure Environment

Decide on the model BEFORE coding.

If user hasn't specified a model, use microsoft-foundry skill to list deployed models or help deploy one.

ALWAYS create/update .env file:

FOUNDRY_PROJECT_ENDPOINT=<project-endpoint>
FOUNDRY_MODEL_DEPLOYMENT_NAME=<model-deployment-name>

• Standard flow: Populate with real values from user's Foundry project
• Deferred Config: Use placeholders, remind user to update before running

Step 3: Implement Code

All three are required by default:

1. Agent/Workflow code: Use gathered context to structure the agent or workflow
2. HTTP Server mode: Wrap with Agent-as-Server pattern from agent-as-server.md — this is the default entry point
3. Debug configs: Generate .vscode/launch.json and .vscode/tasks.json using templates from debug-setup.md

⚠️ Warning: Only skip server mode or debug configs if the user explicitly requests a "minimal" or "no server" setup.

Step 4: Install Dependencies

1. Generate/update requirements.txt

   
   # pin version to avoid breaking changes

agent framework

agent-framework-azure-ai==1.0.0b260107 agent-framework-core==1.0.0b260107

agent server (for HTTP server mode)

azure-ai-agentserver-core==1.0.0b10 azure-ai-agentserver-agentframework==1.0.0b10

debugging support

debugpy agent-dev-cli

2. Use a virtual environment to avoid polluting the global Python installation

> ⚠️ **Warning:** Never use bare `python` or `pip` — always use the venv-activated versions or full paths (e.g., `.venv/bin/pip`).

### Step 5: Verify Startup (Run-Fix Loop)

Enter a run-fix loop until no startup errors:

1. Run the main entrypoint using the venv's Python (e.g., `.venv/Scripts/python main.py` on Windows, `.venv/bin/python main.py` on macOS/Linux)
2. **If startup fails**: Fix error → Rerun
3. **If startup succeeds**: Stop server immediately

**Guardrails**:
- ✅ Perform real run to catch startup errors
- ✅ Cleanup after verification (stop HTTP server)
- ✅ Ignore environment/auth/connection/timeout errors
- ❌ Don't wait for user input
- ❌ Don't create separate test scripts
- ❌ Don't mock configuration

### Step 6: Documentation

Create/update `README.md` with setup instructions and usage examples.

## Error Handling

| Error | Cause | Resolution |
|-------|-------|------------|
| `ModuleNotFoundError` | Missing SDK | Run `pip install agent-framework-azure-ai==1.0.0b260107` in venv |
| `AgentRunResponseUpdate` not found | Wrong SDK version | Pin to `1.0.0b260107` (breaking rename in newer versions) |
| Agent name validation error | Invalid characters | Use alphanumeric + hyphens, start/end with alphanumeric, max 63 chars |
| Async credential error | Wrong import | Use `azure.identity.aio.DefaultAzureCredential` (not `azure.identity`) |