Microsoft Agent Framework

Trigger On

• building or reviewing .NET code that uses Microsoft.Agents.*, Microsoft.Extensions.AI, AIAgent, AgentThread, AgentSession, or Agent Framework hosting packages
• choosing between ChatClientAgent, Responses agents, hosted agents, custom agents, Anthropic agents, workflows, or durable agents
• authoring preview-era Microsoft.Agents.AI.Workflows.Declarative* packages or wrapping a workflow with workflow.AsAIAgent()
• adding tools, MCP, A2A, OpenAI-compatible hosting, AG-UI, DevUI, background responses, or OpenTelemetry
• migrating from Semantic Kernel agent APIs or aligning AutoGen-style multi-agent patterns to Agent Framework
• using Anthropic Claude models (haiku, sonnet, opus) via AnthropicClient or through Azure Foundry with AnthropicFoundryClient

Workflow

1. Decide whether the problem should stay deterministic. If plain code or a typed workflow without LLM autonomy is enough, do that instead of adding an agent.
2. Choose the execution shape first: single AIAgent, explicit programmatic Workflow, workflow-as-agent wrapper, declarative workflow when YAML portability is explicitly required, Azure Functions durable agent, ASP.NET Core hosted agent, AG-UI remote UI, or DevUI local debugging.
3. Choose the agent type and provider intentionally. Prefer the simplest agent that satisfies the threading, tooling, and hosting requirements.
4. Keep agents stateless and keep conversation or long-lived state in provider-owned session objects. Most persistence guidance still centers on AgentThread, while newer middleware and background-response examples may surface AgentSession. Treat both as opaque provider-specific state.
5. Add only the tools and middleware that the scenario needs. Narrow the tool surface, require approval for side effects, and treat MCP, A2A, and third-party services as trust boundaries.
6. For workflows, model executors, edges, request-response ports, checkpoints, shared state, and human-in-the-loop explicitly rather than hiding control flow in prompts.