Phase 6: Quality Gates & CI/CD for AI Code · 45 min · Claude Code · GitHub Copilot · Python

Multi-Agent Test Generation in CI

One agent's tests passing proves the code satisfies that agent's blind spots. Two independent agents agreeing is real evidence — and disagreeing is a real signal.

Real-world application: Running two different agents' test suites against the same function and comparing coverage, not just pass/fail, is the concrete practice behind the generator/validator diversity principle this course has built toward since c12-00-4.

What you will learn

Explain why two independently-generated test suites agreeing is stronger evidence than one suite passing
Identify what a disagreement between two agents' test suites signals and why it warrants investigation, not a coin flip
Compute comparative test metrics: test count, pass/fail rate, and coverage delta between two suites
Detect a real bug that one agent's test suite catches and another's entirely misses

Introduction

Multi-Agent Test Generation in CI

Objective

Run two independently-generated test suites against a real, subtly-buggy implementation and compute test count, pass rate, and coverage delta between them.

Unlock the full lesson

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