The AI Agent Engineer's Handbook: Designing, Building, Evaluating, Securing, and Operating Production-Grade AI Agents with Large Language Models — The Complete Guide to Agentic AI

The AI Agent Engineer’s Handbook

Designing, Building, Evaluating, Securing, and Operating Production-Grade AI Agents with Large Language Models — The Complete Guide to Agentic AI

Large language models can write, reason, retrieve information, use tools, and interact with software systems. But turning those capabilities into dependable AI agents is not a prompting exercise. It is a systems-engineering problem.

The AI Agent Engineer’s Handbook presents a practical, architecture-first approach to building production-grade AI agents—from the first model call to enterprise-scale agent platforms.

The book begins by establishing the foundations of agent engineering and the role of large language models as probabilistic components inside deterministic software systems. It then explores the architecture required to give agents controlled access to context, memory, knowledge, tools, workflows, and external systems.

You will learn how to engineer the complete agent lifecycle: constructing context, managing state, designing memory, implementing retrieval-augmented generation, planning multi-step tasks, selecting and invoking tools, orchestrating workflows, evaluating decisions, and adapting execution based on observations.

The focus then shifts from capability to engineering quality. You will learn how to test agent trajectories, evaluate task success, measure planning and tool-selection quality, design reliable recovery mechanisms, handle timeouts and failures, implement observability and tracing, detect runaway execution, and understand the real performance and cost characteristics of agent workloads.

Security is treated as a core architectural concern. The book examines prompt injection, indirect prompt injection, tool poisoning, memory poisoning, data exfiltration, privilege escalation, identity propagation, authorization, least-privilege design, secrets management, sandboxing, code execution, browser security, policy enforcement, and human approval gates.

The production chapters address the realities that emerge after the prototype works: deployment architecture, model serving, persistent state, queues, background workers, CI/CD, versioning, canary releases, rollback, incident response, disaster recovery, scaling, model routing, token optimization, context optimization, caching, capacity planning, latency engineering, and cost per successful task.

The final part moves from individual applications to enterprise agent platforms, covering agent registries, model and provider abstraction, tool and capability infrastructure, shared memory, evaluation platforms, observability, security and policy infrastructure, human approval systems, lifecycle management, and platform governance.

Inside this book, you will learn how to:

• Design production-grade AI agent architectures
• Engineer context, state, memory, and RAG systems
• Build planning, reasoning, tool-use, and workflow capabilities
• Evaluate complete agent trajectories and task outcomes
• Engineer reliability, recovery, verification, and failure containment
• Build security boundaries around autonomous actions
• Implement identity, authorization, sandboxing, and policy controls
• Deploy, monitor, scale, and optimize agent workloads
• Control model, token, infrastructure, and execution costs
• Design enterprise platforms for operating AI agents at scale

At the center of the book is a simple engineering principle: the model proposes; the runtime validates; authorization decides; policy constrains; the system executes; observation updates state; verification determines what happens next.

This is a handbook for engineers and architects who want to understand what it takes to move from impressive AI demonstrations to reliable, secure, observable, and production-ready agentic systems.