# observability

When an agent breaks in production, observability hands you a gorgeous autopsy — every call, latency, and token, but not why it broke or how to fix it. The fix is a loop that runs itself: failure → approved patch → locked-in regression test. Opik is just the example; the point is the loop.

OpenAI quietly open-sourced Euphony — a browser-based viewer for Harmony chats and Codex session logs (Apache 2.0). Four telling details buried in the source: a 2-line AGENTS.md, gpt-tokenizer as a runtime dep, translation needing the user's own API key, and a self-written SSRF warning.

LangChain's conceptual guide breaks down agent improvement into a trace-centric loop: collect traces, enrich them with evals and human annotations, diagnose failure patterns, fix based on observed behavior, validate with offline eval, then deploy — each cycle starting from higher ground.

The biggest blind spot in AI agent development is 'tweaking in the dark.' Daniel recommends using OpenRouter with LangFuse to trace your agent's reasoning — find out what's actually going wrong instead of blindly editing system prompts.