Building Reliable Generative AI Agents

AI agents are easy to demonstrate. Reliable agents are engineered.

Generative AI agents can retrieve knowledge, call tools, coordinate workflows, and take action across real business systems. But once an agent moves beyond a controlled demonstration, probabilistic behavior, unsafe tool use, prompt injection, hidden state, rising costs, and silent failures become engineering problems—not merely prompting problems.

Building Reliable Generative AI Agents gives developers, software architects, engineering leaders, and technical decision-makers a practical, framework-neutral blueprint for creating agentic systems that can be evaluated, secured, observed, and scaled.

Inside, you will learn how to:

• Define bounded agent jobs and measurable success criteria • Separate model judgment from deterministic safeguards • Design secure tool contracts, permissions, and approval gates • Manage context and memory without uncontrolled state drift • Build evaluation suites, regression gates, and observability • Defend agents against prompt injection and untrusted content • Control latency, token costs, retries, and failure recovery • Introduce human oversight at high-risk decision points • Expand autonomy only when production evidence supports it

Through practical design patterns and real-world examples, Micheal Phillips explains how to transform a promising AI prototype into a dependable production system.

Whether you are building a retrieval agent, tool-using assistant, multi-agent workflow, or enterprise AI platform, this book will help you make better architectural decisions before failures become expensive.

Reliable agents are not built with prompts alone. They are engineered as secure, measurable systems.

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