AI Performance Engineering:
How Agentic AI is Transforming Load Testing
A builder's guide to AI-powered performance testing, from a multi-agent platform that turns 25 minutes of manual correlation into 75 seconds, an ebook written by the engineer who built it.
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Format
Digital Ebook (read online or download) -
115 Pages
Lived Experience -
8 hours
Estimated Read -
2 Minute
Intro Movie
What This Guide Is
This is an honest account of what happens when you build AI agents to automate the hardest parts of load testing: correlation, script generation, QA validation, and self-healing.
The book covers:
- How AI changes the economics of performance testing
- A real multi-agent architecture built and used in production
- The engineering decisions, tradeoffs, and wrong turns along the way
This is not a theoretical overview or a vendor pitch. Every claim is
grounded in a real platform the author built, broke, and rebuilt.
Free Sample Available
You can download a 26‑page free sample without registration.
Who this book is for
This book is written for:
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Performance engineers and architects working on complex, distributed systems
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Engineering leaders responsible for reliability, scalability, and risk
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QA leaders moving from tool-driven testing to engineering-led performance strategy
Not for:
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Beginners looking for basic load testing tutorials
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Tool-specific certifications or vendor documentation
What You will Gain
This guide supports better decisions, not step‑by‑step implementation.
AI performance beyond models
Understand performance at system level, not just model level.
System‑level bottleneck visibility
Identify risks across pipelines, infrastructure and runtime behaviour.
Informed trade‑off decisions
Balance accuracy, latency, cost and scalability with confidence.
Fewer performance misconceptions
Avoid common traps in AI performance testing and optimisation.
Clarity on AI testing maturity
Decide when AI‑based testing is production‑ready — and when it is not.
Business‑aligned performance thinking
Frame technical performance decisions in terms leaders understand.
What The Full Ebook Covers
The complete book expands far beyond the free sample and includes:
- Why correlation remains performance testing’s hardest problem
- Multi‑layer correlation strategies for AI systems
- From HAR files to production‑grade performance tests
- Agent‑based testing concepts and architectures
- Autonomous and self‑healing testing approaches
- Evaluating AI testing options and trade‑offs
- Building your own AI testing pipeline
- The future of AI testing and performance engineering
- Practical guidance to get started
Plus:
- FAQs
- AI testing quick references
- Glossary
How to use this guide
This is not a linear textbook.
You can:
- Read it end‑to‑end for full context
- Jump to specific chapters as a reference
- Use it as a shared decision‑support document in teams
A short introductory video provides orientation.
An optional closing video frames next steps.
Standardize telemetry and tooling decisions
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Meet the instructor
David Campbell
David is a performance engineering practitioner focusing on complex, performance‑critical systems, including AI‑driven architectures.
His work centres on:
- system‑level performance thinking
- AI performance beyond benchmarks
- bridging experimentation and production reality
This guide reflects hands‑on industry experience, not academic theory.
The Story of a Performance Engineering Expert Company
About Loadmagic
Loadmagic specialises in performance engineering and load testing for modern, complex systems.
The perspectives in this guide are grounded in:
- real production environments
- performance‑critical architectures
- system‑level trade‑offs beyond tooling
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Discover The Book
If AI performance is a business‑critical concern for your organisation, this guide provides the conceptual foundation.
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Build AI Engineering at Your Context
For hands‑on performance engineering, load testing and validation of complex AI systems, explore Loadmagic.
Course Lessons
Download the 26‑page sample
Access the full AI Performance Engineering guide
FAQ – Frequently Asked Questions
What is this book about?
This is a practical ebook on AI Performance Engineering: what changes when you use agentic AI to accelerate load testing (correlation, script generation, QA validation, and self-healing), and what still requires human expertise.
Who is this for?
Performance engineers, SRE/DevOps engineers, QA/automation engineers, and engineering leaders who want a clear, production-tested view of where AI agents help in performance testing and where the limits are.
What will I learn inside the ebook?
You will learn the three-layer correlation architecture (observe, decide, prove), see a time-motion comparison of manual vs AI-assisted workflows, and follow how five agents (Carrie, Rupert, Suzy, Quinn, and George) collaborate - including lessons from a "God Mode" autonomy experiment.
Is this a tool-specific book?
No. The focus is on workflows, architecture, and decision-making. The patterns apply whether you use commercial performance tooling, open-source stacks, or build parts in-house.
How do I access the ebook after enrolling?
After enrollment, you can read the ebook online inside the course module and download it for offline use, along with the supporting materials, all in one place.
Does it include guidance for build vs buy?
Yes. The ebook includes a build-vs-buy guide for teams considering whether to build AI agent capabilities themselves or adopt an existing platform.
Do You Have Any Questions Before You Get Started?
Learn how we work, check out our frequently asked questions, or feel free to write to us!
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