Stress-Testing AI Agents

Evaluation, Fault Injection, Recovery, Guardrails, and Production Reliability.

von Trent B Presley

372 Seiten, Taschenbuch

Kurzbeschreibung des Verlags

AI agents can look impressive in a demo and still fail badly in production.A polished response does not prove that an agent chose the right tool, respected permissions, handled failure safely, preserved state correctly, or completed the real task. Once an AI system begins calling tools, updating records, retrieving data, making decisions, and triggering external actions, reliability becomes an engineering problem-not just a prompting problem.Stress-Testing AI Agents is a practical guide to evaluating, breaking, measuring, and strengthening agentic systems before real users and real-world failures expose their weaknesses.Built around a hands-on Python reference agent, this book shows you how to move beyond one-off demonstrations and create a repeatable reliability workflow for modern AI agents. You will learn how to define correct behavior, capture execution traces, validate tool calls, inspect agent trajectories, measure repeated-run performance, simulate operational failures, test memory and retrieval, enforce guardrails, and prevent regressions before deployment.Inside, you'll learn how to: - Define measurable success criteria, invariants, and failure conditions for AI agents- Build a reusable agent testing harness with Python and pytest- Validate tool selection, arguments, schemas, ordering, and side effects- Evaluate real task outcomes instead of trusting an agent's final response- Inspect execution trajectories for loops, wasted actions, drift, and unsafe behavior- Measure repeated-run reliability and compare agent versions statistically- Build high-quality evaluation datasets and regression suites- Use LLMs as evaluators without treating them as infallible judges- Stress-test agents with paraphrases, ambiguity, edge cases, and adversarial inputs- Inject timeouts, malformed responses, stale data, authentication failures, and other operational faults- Test retries, idempotency, checkpointing, fallback behavior, and recovery- Evaluate memory, persistent state, retrieval-augmented systems, and long-horizon tasks- Test prompt injection, permissions, data boundaries, and human approval controls- Track reliability, latency, cost, intervention rate, and production failures- Add agent evaluations to CI/CD and turn real incidents into permanent regression testsThis is not a book about making agents look intelligent.It is about building evidence that they are reliable enough to trust.Whether you are an AI engineer, LLM developer, software engineer, QA professional, MLOps engineer, platform engineer, technical founder, or anyone responsible for deploying agentic systems, this book gives you a practical framework for moving from experimental prototypes to production-ready AI systems.If your agent can take actions in the real world, you should know how it fails before your users do.

• ISBN: 9798193319931
• Sprache: Englisch
• Erscheinungsdatum: 17.08.2026
• Größe: 254 x 178 mm
• Verlag: Amazon Digital Services LLC - Kdp