AI Engineering/Phase 14/Lesson 30

Eval-Driven Agent Development

Anthropic's guidance: "start with simple prompts, optimize them with comprehensive evaluation, and add multi-step agentic systems only when needed." Evaluation is not the last step. It's the outer loop that drives every other choice in Phase 14.

Learn + Build 1 hr Python (stdlib)

Prerequisites: All of Phase 14.

#Learning Objectives

Name the three evaluation layers — static benchmarks, custom offline, online production — and what each is for.
Explain the evaluator-optimizer tight loop.
Describe the 2026 best practice: evals live next to code, run in CI, gate PRs.
Connect every Phase 14 lesson to the eval case it generates.

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The text and code of this lesson are based on AI Engineering from Scratch by Rohit Ghumare, MIT licensed.