Enterprise AI Agents in Production: Build and Secure Them

Your CTO just asked the question. The team wants to put an AI agent in front of customers. Not a chatbot. An agent that can read from the CRM, decide, and act. Payments, cases, refunds, entitlements.

Is it safe to ship?
What is your plan?
Whose name goes on the decision?

The old security playbook was not written for this. Reviewing prompts and logging chatbot sessions is not enough once an AI system can pick a tool, call an API, and change state in production. Something that can act needs an identity, a boundary, an audit trail, and a way to fail safely. It also needs someone who has thought carefully about what happens when the model decides badly, when the retrieval layer returns poisoned content, or when a tool wrapper leaks a token.

Securing Enterprise AI Agents is the field guide that answers those questions. Six parts, twenty-one chapters, worked examples drawn from financial services because that is what regulated builds look like. Part I reframes the risk model. Part II is the reference architecture for a production agent, from human user down through the orchestrator, the model, the tool wrapper, MCP, policy, and data layer. Part III turns evals and observability into the release control your CI does not have yet. Part IV is secure RAG, including the parts most teams skip. Part V is coding agents in professional teams. Part VI is deployment, policy as code, incident response, and how to run all of this as one AgentSecOps discipline.

The book is for the person who owns the sign-off. The engineer whose demo now has to go live. The architect triaging a stack of agent proposals. The security lead asked to bless something they cannot fully see. The engineering director choosing where the next quarter of platform investment goes.

No AGI speculation. No vendor comparison. No "challenges and opportunities" bullet lists at the end of every chapter. You will leave with a seven-layer reference architecture you can defend to an auditor, a production readiness checklist you can run in an afternoon, and a working definition of bounded AI autonomy that keeps the model useful without giving it the whole surface.

A Best-Selling Book on LeanPub for weeks since releasing. A mandatory guide for building Claude production AI agents.

This is a Claude Code production agents book for engineers who need agents that can run with tools, permissions, MCP servers, evals, observability, and cost controls in real systems.

Most Claude Code tutorials stop at "hello world." This book covers what happens after that: when your agent needs to run reliably in production, at scale, in environments where failure has real consequences.

Written by an AI engineer who builds Claude Code agent systems for regulated financial institutions, it walks through the full production stack.

Part I covers the agent loop, context management, and model selection.

Part II builds the primitive layer: tools, hooks, skills, MCP servers, and plugins.

Part III bridges the CLI to the Claude Agent SDK, covering the dispatch loop, session management, and tool registration for headless deployment. Part IV tackles governance: permissions, sandboxing, secrets, audit trails, and managed settings.

Part V covers evals, LLM-as-judge patterns, observability, cost engineering, and failure modes.

Part VI puts it together with team workflows, deployment patterns, multi-agent orchestration, and a full walkthrough of Anthropic's open-source financial services reference agents (the production-grade agent templates Anthropic released in May 2026).

Thirty-one chapters, five practical appendices (including a ninety-day production-readiness checklist, an eval starter kit, and an MCP server audit template), and code extracted from production systems.

If you are a senior AI engineer, technical lead, or architect evaluating Claude Code for production use, this is the reference that will save you months of trial and error.