You Can Learn AI Agent Observability & Eval In 20 Min | LLM Ops, Traces, Treg.to, Waku.one

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Издатель Oct 6, 2026

Try https://treg.to, Memory https://waku.one, Me https://seanchen.io, Open Source https://github.com/shenseanchen/waku-agent | Your agent just spent your money and you probably cannot say where it went. Observability is the camera that records what ran, how long and what it cost. Eval is the referee that decides whether the run was good enough to ship. I draw both out on the whiteboard, step by step, so you can follow it whether or not you come from a technical background, then I run two real research tasks live and trace every tool call, every token and every cent inside my own dashboard. On the second run, 53 of the 54 cents went to the model and 1 cent covered all seven tool calls.

Here's what we cover:

- Observability is a camera: what ran, how long, what it cost
- Eval is a referee: AI, code or a human judging whether the run was good
- The three things every call passes through: model, tools and memory
- The gateway and metering proxy, and why every step leaves a trace
- Reading a real trace: 5 agent loops, 10 tool calls with Treg.to, 70 seconds, 29 cents
- Six eval checks, including one that catches the agent lying about its own spend
- A second run at 54 cents, and the one tool call that failed

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