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Unit Testing's Eval Twin

If you've got a software background and are starting to look into integrating agentic AI into your solutions, it can be quite a paradigm shift. Agents are unpredictable black boxes, which can feel intractable at times. In this article, I attempt to draw on some intuition you may have about traditional testing, to write effective evals to produce predictable and well-aligned agents.

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Unit Testing's Eval Twin
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Under the source content permission, this page provides an editorial summary, key points, and a link to the original article.

Key points and analysis

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Key points

  • Unit tests evaluate individual steps in an agent loop.
  • Integration tests evaluate full agent loops with tool calls.
  • End-to-end tests validate the entire system's behavior.
  • Design rubrics with weighted criteria and use LLM-as-a-judge for fuzzy evaluations.

Highlights and analysis are generated automatically and may contain errors. Check the original source.