Evaluating AI Agents with LLM-as-a-Judge Harness in Python
ai•Jul 17, 2026

Evaluating AI Agents with LLM-as-a-Judge Harness in Python

Learn how to build a lightweight evaluation setup to test AI agents before making changes, using a Python script, test cases, rule-based checks, and an LLM-as-judge.

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Key summary

This tutorial shows how to evaluate a local AI agent with a simple, repeatable evaluation harness using LLM-as-a-judge in Python.

TL;DR

  • Agent evaluation is the practice of running an AI agent against a fixed set of inputs and scoring the outputs.
  • LLM-as-a-judge is a method of scoring an agent's output using a separate LLM.
  • A useful eval has three parts: test cases, checks, and a summary.
  • Rule-based checks are cheap, fast, and deterministic, while LLM-as-a-judge is useful for fuzzy things.
  • The tutorial uses LangChain v1, Ollama, Qwen, and Python to build a repeatable evaluation harness.
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