Building a Local AI Agent in Go -- Part 7: Observability
Part 7 of 7. The four visibility layers that ship with the agent -- verbose CLI logs, the live web dashboard + SSE event stream, OpenTelemetry traces, and OpenTelemetry and Prometheus metrics.
Jürgen Fey
CTO/Co-Founder @SmartTechLabs - System Architect, AI, ML, LLM Consulting, Embedded, Data Spaces, Observability, Digitalization Consulting, WebRTC&VR/AR/MR. Start-Ups. Consulting
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Part 5 of 7. Extending the agent with narrow MCP servers, smart web fetching via llms.txt, Lemonade Server management for AMD, running the full system end-to-end, and operational troubleshooting.
Building a Local AI Agent in Go -- Part 4: Inside the Codebase
Part 4 of 7. A file-by-file walkthrough of every source file in the agent: entry point, config, agent loop, LLM client, MCP manager, tool parser, events, web dashboard, REST API, HITL and more..
Building a Local AI Agent in Go -- Part 3: The Agent Loop
Part 3 of 7. The core agent loop: how the LLM and tools alternate, ten distinct termination paths, per-query safety limits, and handling models that encode tool calls differently.
Building a Local AI Agent in Go -- Part 2: Understanding MCP
Part 2 of 7. What MCP is, how the JSON-RPC protocol works under the hood, how tools plug into the agent, and how MCP fits into the broader agent ecosystem.
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Build your own Agent in Golang. Use local and cloud LLM models.
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