About this book:

This book provides a comprehensive guide to engineering reliability, performance, and compliance for intelligent agents. Moving beyond traditional deterministic software testing, the text outlines a specialized framework for managing the non-deterministic nature of large language models (LLMs). It advocates for a multi-layered testing strategy that combines unit testing of individual tools and policies, integration testing of planners and toolchains, and high-fidelity simulations to explore emergent behaviors in dynamic environments.

The book emphasizes the necessity of deep observability, treating it as a first-class design constraint. It details the instrumentation of prompts, tokens, and tool calls, alongside the implementation of distributed tracing to map multi-step and multi-agent workflows. These signals feed into monitoring pipelines designed to detect "behavioral drift"—subtle shifts in agent performance or alignment caused by model updates or data evolution. By establishing statistical baselines and using semantic similarity metrics, teams can identify degradation that traditional binary tests might miss.

Safety and risk management are central themes, with dedicated chapters on red-teaming methodologies to probe for hallucinations, bias, and jailbreak attempts. The author argues for a "defense in depth" approach, utilizing automated guardrails, human-in-the-loop evaluations, and rigorous data management of prompts and "golden" datasets. To bridge the gap between lab environments and production, the book details progressive delivery techniques such as canary releases, shadow traffic, and replay testing, which allow for controlled exposure and risk mitigation.

Finally, the text addresses the operational and governance challenges of running agents at scale. It links technical performance and latency Service Level Objectives (SLOs) to economic viability and FinOps, offering strategies to characterize and optimize token costs. The concluding chapters provide an operational playbook for incident response, blameless postmortems, and the establishment of governance frameworks. This ensures that agents are not only intelligent but also auditable, transparent, and accountable throughout their lifecycle.

What You'll Find Inside:

• Practical testing methodologies covering unit, integration, scenario, simulation, adversarial, and user-in-the-loop evaluation to ensure agent reliability and safety.
• Techniques for managing model non-determinism through seeding, sampling controls, variance measurement, and behavioral drift detection.
• End-to-end observability design: instrumenting prompts, token usage, and tool calls, and leveraging logs, metrics, and traces for deep workflow insight.
• Risk‑aware deployment strategies including canary releases, shadow traffic, replay testing, A/B testing, and progressive delivery to validate changes with minimal user impact.
• Defining agent quality via metrics, SLOs, and risk profiles, with focused safety testing for hallucinations, harmful content, jailbreaks, and compliance considerations.

Who's It For:

The book is for engineers, data scientists, SREs, product managers, and security/compliance professionals who are building and operating agentic systems in production. It assumes familiarity with modern software delivery practices but does not require prior specialization in agents, providing actionable patterns and checklists that can be applied immediately.