AI Performance Engineering

AI Performance Engineering is a practical, honest account of what happens when you build AI agents to take on the hardest, most repetitive parts of load testing: correlation, script generation, QA validation, and self-healing. It is written by someone who built the platform he is describing, with minute-by-minute data, architecture decisions, and a few stories about what went wrong along the way.

You will meet George, Carrie, Rupert, Suzy, and Quinn — the five AI agents at the heart of LoadMagic — and see how their roles, personalities, and chain reactions emerged from real engineering problems. You will see a stopwatch time-and-motion study comparing manual correlation with AI-powered correlation (75 seconds versus 25 minutes on a 9-request flow, and projections out to enterprise scale). You will read the God Mode story: what happened when one agent was given too much autonomy, and what that taught us about trust, failsafes, and the limits of AI judgement.

The book covers the three-layer architecture that makes self-healing possible, the quality gate that decides when a script is ready to run under load, the four approaches you can choose from when adding AI to your own testing workflow, and a practical blueprint for readers who want to build their own pipeline from the ground up.

Every claim is measured where measurement is possible, and honest about limits where it is not. No prior AI experience required. 25 years of performance testing experience distilled into around 140 pages.