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

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

Agent/Workflow code: Use gathered context to structure the agent or workflow
HTTP Server mode: Wrap with Agent-as-Server pattern from agent-as-server.md — this is the default entry point
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

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

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:

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)
If startup fails: Fix error → Rerun
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) |