Phase 3: The Generate-Validate Loop · 45 min · Claude Code · Python

Debugging AI-Generated Code

If the same test keeps un-breaking and re-breaking across attempts, more patches won't fix it — the approach needs to change.

Real-world application: Recognizing agent thrashing from a sequence of fix attempts — not just eventually noticing you're stuck — is the debugging judgment that keeps a bad session from burning hours before someone steps back.

What you will learn

Apply the predict-before-reveal habit to debugging: form a hypothesis before asking an agent to fix an error
Paste errors with enough context (full traceback, command, surrounding code) for a fix attempt to be well-targeted
Use an explain-and-retry loop to distinguish a debugging session that's converging from one that's thrashing
Decide when to abandon incremental patching and restart from the spec instead

Introduction

Debugging AI-Generated Code

Objective

Detect whether a sequence of debug attempts is converging toward zero failures or thrashing between the same broken states, and recommend patch vs. restart accordingly.

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You've read the first 2 sections. The rest of this lesson covers The Problem, The Concept, Build, Check Yourself, Key Terms & Next — plus a hands-on lab, quiz, and project artifact.

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Key terms in this lesson

Agent