The original title is "How to Build an Evaluation Harness for Your AI Agent (So It Doesn't Break in Production)"

Short summary

This tutorial walks through building a Python-based evaluation harness for AI agents using four graders: Truth (correct tool calls), Path (sane trajectory), Judge (LLM-as-judge for fuzzy outcomes), and Gate (CI regression detection). It explains why manual testing fails for agents—demo bias, non-determinism, and silent regressions—and provides a starter dataset pattern sourced from real failures. The approach is framework-agnostic and integrates into CI to catch regressions before users do.

• •Four graders: Truth, Path, Judge, and Gate cover tool-call correctness, trajectory sanity, outcome quality, and CI regression
• •Start with 20-50 tasks drawn from real failures with unambiguous pass/fail criteria
• •Framework-agnostic pattern works with LangChain, LangGraph, MCP, or custom agent setups

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