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Agent-Driven Testing for AI Systems: Build Self-Evaluating, Self-Auditing, and Self-Improving AI Agents
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Publisher: Independently Published
Published: Jan 27 2026
Pages: 200
Weight: 0.78
Height: 0.42 Width: 7.00 Depth: 10.00
Language: English
Agent-Driven Testing for AI Systems: Build Self-Evaluating, Self-Auditing, and Self-Improving AI Agents
AI systems don't fail loudly. They fail quietly.Models pass unit tests, ship to production, and then drift, hallucinate, or degrade in ways traditional testing never sees. Static test cases can't keep up with probabilistic systems, fast-moving prompts, or agents that change their own behavior. If you're relying on manual reviews, brittle eval scripts, or post-mortems after users complain, you're already behind.
Agent-Driven Testing for AI Systems presents a practical solution to this problem: testing AI with AI. This book shows how to design agents that continuously evaluate, audit, and improve other agents in real time. Instead of treating testing as a one-time gate, you'll learn how to embed it directly into the system itself, turning quality assurance into a living, adaptive process.
The core idea is simple and powerful. Autonomous evaluator agents generate tests, challenge assumptions, detect regressions, flag silent failures, and feed corrections back into the system. These agents reason over outputs, compare behaviors across versions, monitor drift, and enforce quality standards long after deployment. The result is AI that doesn't just run, but watches itself.
By the end of this book, readers will be able to:
Design self-evaluating AI agents that test outputs, reasoning paths, and tool usage
Build self-auditing systems that detect hallucinations, bias, and performance drift
Implement agent-based regression testing for prompts, tools, and workflows
Create feedback loops where agents improve future behavior based on test outcomes
Replace fragile eval scripts with adaptive, agent-driven test orchestration
Apply these patterns to LLM pipelines, multi-agent systems, and production AI platforms
Written for AI engineers, QA automation leads, and LLMOps specialists, this book focuses on real systems, real failure modes, and real safeguards. No theory for theory's sake. Just repeatable patterns that scale as fast as your AI does.
If you're serious about AI reliability, observability, and long-term performance, this is the missing layer.
Order this book and start building AI systems that test themselves.
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