Enterprise AI Agents in Production: Build and Secure Them

Enterprise AI agents are moving from demos into workflows that touch code, data, tools, tickets, documents, APIs, and customer operations. That changes the security problem. Reviewing prompts, logging chatbot sessions, and writing acceptable-use policies is not enough once an AI system can act.

Securing Enterprise AI Agents shows how to treat agents as operational systems. It covers bounded AI autonomy, AgentSecOps, MCP security, RAG governance, identity controls, approval workflows, audit evidence, evals, observability, incident response, and regulatory readiness as one connected discipline.

The core argument is simple: enterprises do not need maximum autonomy. They need useful agents that can act inside clear boundaries, prove what they did, stay inside policy, and fail safely.

If you are searching for AI agent security, secure AI agents, or practical controls for AI agents in production, this book is the field guide I wish more enterprise teams had before their first agent touched real data, real tools, or real customers.

This book is for CISOs, CIOs, CTOs, enterprise architects, security architects, platform teams, AppSec leaders, AI governance teams, risk owners, auditors, and engineering leaders moving agents from experiments into real enterprise workflows.

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.