foundry-agent-basics
Help a learner build their first AI agent with the Microsoft Agent Framework (MAF) on Microsoft Foundry. Use when the task is creating a basic agent, wiring up FoundryChatClient, choosing a model, running the DevUI, or understanding lesson 1–2 concepts (instructions, tools, threads).
- License 许可
- MIT
- 最近更新
- 2026-10-31
- Stars
- ★ 198
- Forks
- 72
01 前言 Front Matter
- name
- foundry-agent-basics
- description
- Help a learner build their first AI agent with the Microsoft Agent Framework (MAF) on Microsoft Foundry. Use when the task is creating a basic agent, wiring up FoundryChatClient, choosing a model, running the DevUI, or understanding lesson 1–2 concepts (instructions, tools, threads).
03 SKILL.md 正文
Foundry agent basics
Guidance for building a first agent in this course (Lessons 1–2).
Guardrails (always apply)
- Platform: Microsoft Foundry (never say "Azure AI Foundry"). Framework: Microsoft Agent Framework (MAF).
- SDK: use the pinned
agent-frameworkfromrequirements.txtwithFoundryChatClientand the Responses API. Do not useAzureAIClient,AzureAIAgentClient, orAzureOpenAIChatClient. - Model:
gpt-5.1for chat. Nevergpt-4o/gpt-4.1(retired) and never GitHub Models. - Auth:
az login+AzureCliCredential; endpoint/model come from.env(never hardcoded).
What a learner should understand first
- Agent = model + instructions + tools + a thread. Instructions set the persona/task; tools extend capability; a thread carries conversation state.
- Design before code (Lesson 1): identify the scenario, the user, the tools needed, and the success criteria. The course use case is "Developer Onboarding".
- One agent, one job. Lesson 2 builds several small, specialised agents rather than one monolith (employee search, learning recommendation, coding, orchestration, etc.).
Minimal shape of a sample
- Load env with
python-dotenv; read the Foundry project endpoint and model name. - Create a
FoundryChatClientwithAzureCliCredential. - Create the agent with clear
instructions; attach tools if needed. - Run it — most lesson-2 samples expose a local DevUI on a dedicated port (8090+).
How to help
- Point the learner at the closest existing sample in
lesson-2-agent-development/and adapt it, rather than writing from scratch. - Keep instructions specific and scoped; show how to test with a couple of representative prompts.
- Remind them to
pip install -r requirements.txt,az login, and populate.envfirst.
Validate
python -m py_compile <file>.pymust pass.- The sample should start its DevUI and answer a basic prompt end-to-end.
References
lesson-1-agent-design/README.md,lesson-2-agent-development/README.mdMIGRATION-GUIDE.mdfor the current SDK patterns.
04 同仓库更多 Skills
multi-agent-a2a
Help a learner compose agents as networked services using the open Agent-to-Agent (A2A) protocol — expose an agent as an A2A server and consume a remote agent as a peer. Use for Lesson 7 tasks: multi-agent orchestration and A2A client/server wiring.
microsoft-toolbox
Help a learner use Microsoft Toolbox to define tools once and govern them centrally, then consume a toolbox from an agent through a single MCP endpoint. Use for Lesson 6 tasks: tool discovery, registration, governance, and versioning.
mcp-integration
Help a learner give an agent tools through the Model Context Protocol (MCP) — both client-side MCP and Foundry Hosted MCP tools. Use when connecting an agent to an MCP server (e.g. the GitHub MCP server), adding a remote tool, or reasoning about MCP approval/governance.
hosted-agent-deployment
Help a learner deploy an agent to production as a Microsoft Foundry Hosted Agent, and decide when Capability Hosts are needed. Use for Lessons 4–5: hosted deployment, thread/memory persistence, bring-your-own storage, and enterprise governance.
agent-evaluation
Help a learner evaluate and observe an AI agent on Microsoft Foundry — quality/safety evaluators, tracing, and reading results. Use for Lesson 3 tasks: measuring agent performance, adding observability, or interpreting evaluation output.