Is Your AI Agent Actually Working? Evals, Observability & Debugging for Production Agents

₹900

Your agent passed the demo. Does it still work on the 500th request, after a prompt tweak, on the day your model provider ships an update? Agents fail differently from normal software. A bug does not throw an exception; it returns a confident, well-formatted, wrong answer with HTTP 200. This 39-page field guide translates the testing and monitoring discipline you already know into something that works for non-deterministic, multi-step agents: tracing replaces stack traces, eval datasets replace unit-test fixtures, graders replace assertions. What’s inside - Why agents are hard to test: non-determinism, compounding errors and silent failure - Error analysis first: read 50 to 100 traces and build a failure taxonomy before writing a single metric - Tracing and observability with OpenTelemetry GenAI conventions - Building an eval dataset from real failures, with negative cases - Code-based graders that check final state, tools, arguments and budgets - LLM-as-a-judge done properly: narrow binary judges, bias fixes and TPR/TNR calibration - Metrics that matter: pass@k vs passk, confidence intervals, cost and p95 latency - Evals in CI/CD and production: smoke suites, canaries and a weekly trace-review loop - An end-to-end worked example fixing a support-triage agent - A pre-ship checklist, pitfalls list and four week learning path Every chapter has a Key Points summary and a practical Python example you can adapt to LangGraph, the OpenAI Agents SDK, the Claude Agent SDK or plain API calls.