AI Tools For Testing: How to Choose the Right Approach for QA

Building for MCP pushed QA Wolf to replace app navigation with focused, task-oriented views. See how designing for coding agents simplified the whole product.

We followed the same customers for 21 months and 50,000 AI-generated PRs: they deployed about 30% more often, reported bugs fell 20%, and bugs per deploy dropped by more than a third.

Claude Code and Codex can write a Playwright test. At hundreds of PRs a week, QA also needs coverage decisions, a test fleet, and agents that run unattended.

Agent sessions don't behave like cloud jobs. How we rebuilt our infrastructure so every QA Wolf AI agent gets its own machine with a real filesystem, shell and browser.

How we built real-time WebRTC streaming for unattended test runners across web, Android, and iOS—and why one requirement, "it has to feel live," set off a chain reaction.

E2E tests need to be deterministic, but not every variation is a failure. Here's how QA Wolf uses Jev to let wording and routes vary while Playwright enforces the requirements that can't.

AI slop costs software teams $1.17 trillion a year. We added up the price of unverified AI code across 6 categories — from wasted tokens to churn.

Learn how we built a product-mapping AI to autonomously explore web apps, outline test coverage, and keep a test suite accurate as engineers release new features.