A guided introduction to the Microsoft Agent Framework in Go, building up the core concepts one focused lesson at a time.
Why I learned the whole framework in Go by writing one runnable lesson per concept, against Azure AI Foundry, instead of reading the docs top to bottom.
I learned the whole Microsoft Agent Framework in Go by building one runnable lesson per concept against Azure AI Foundry. Here is the 12-track map.
The minimal loop in Go: a Foundry provider, an Agent with instructions, run collected and streamed — and what RunText hands back.
The minimal Microsoft Agent Framework loop in Go: a foundryprovider agent, DefaultAzureCredential, and one RunText you either Collect or range over as a Go 1.23 iterator.
Wrap a typed Go function as a tool, let the model call it, and compose whole agents as tools.
Wrap a typed Go function as a tool with functool.MustNew, let the model call it, and compose whole agents as tools with agenttool.New.
A Session threads history into each run; a ContextProvider carries memory across sessions — and because a Session is JSON, both survive a process restart.
A Session threads history into each run and a ContextProvider carries memory across sessions. Because a Session is JSON, both survive a process restart.
Decoding typed structs from a run, draining a streamed response, and sending an image with `RunMessage`.
Decode typed structs from a run, drain a streamed response by ranging it, and send an image with RunMessage and DataContent.
A guardrail that blocks a run before the model, chained ahead of a logger — and the upstream nil-update panic I fixed along the way.
A guardrail middleware that blocks an Microsoft Agent Framework Go run before the model, chained ahead of a logger — plus the tool-approval nil-update panic I fixed upstream in PR #472.
Wrap every run in an OpenTelemetry span, gate risky tool actions behind a permission handler, and swap model providers behind one agent.Agent.
Wrap every Microsoft Agent Framework run in an OpenTelemetry span, gate risky tool actions behind a permission handler, and swap Anthropic, OpenAI, Gemini, Copilot, and Azure behind one agent.
The graph model underneath every multi-agent app: executors as nodes, edges as data flow, and typed events streaming out of `WatchStream` as it runs.
The Microsoft Agent Framework workflow model in Go: executors bound to IDs, AddEdge wiring, WithOutputFrom, and typed WatchStream events - plus an upstream route-builder fix.
Agents as graph executors in Go: switch routing, fan-out/fan-in barriers, and mixing typed function nodes with agent nodes.
Agents as Go workflow executors: switch routing, fan-out and fan-in barriers, and mixing typed function nodes with agent nodes in one graph.
The prebuilt orchestration builders in agent-framework-go — Sequential, Concurrent, Group Chat — plus wrapping a whole workflow as one agent.
The Sequential, Concurrent, and Group Chat orchestration builders in agent-framework-go, plus wrapping a whole workflow as one nestable agent.
Durable workflows in Go: checkpoint and rehydrate, pause on a RequestPort for a human, nest sub-workflows, and coordinate through scoped shared state.
Durable Microsoft Agent Framework workflows in Go: checkpoint and rehydrate a fresh graph, pause on a RequestPort for a human, nest sub-workflows, and coordinate via scoped shared state.
Wrapping Go agents in A2A, MCP, and AG-UI transports, then a full client/server app that ties the whole series together.
Wrap Go agents in A2A, MCP, and AG-UI transports, then run the end-to-end client/server capstone where a host agent uses remote specialists as tools.
This series is part of a larger body of work by Pratik Dhanave, an Agentic AI Architect writing about production AI systems, distributed systems, and cloud-native engineering. Explore all course series, browse every post, or find topics via the tag index.