Autonomous AI Agents Engineering: Build Reliable Production AI Agents with Python, Planning, Memory, Tools, RAG, MCP, Multi-Agent Systems, Evals, Security, and Human Oversight
Autonomous AI Agents Engineering: Build Reliable Production AI Agents with Python, Planning, Memory, Tools, RAG, MCP, Multi-Agent Systems, Evals, Security, and Human Oversight
Autonomous AI Agents Engineering: Build Reliable Production AI Agents with Python, Planning, Memory, Tools, RAG, MCP, Multi-Agent Systems, Evals, Security, and Human Oversight - McLucas, Cameron
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Autonomous AI Agents Engineering: Build Reliable Production AI Agents with Python, Planning, Memory, Tools, RAG, MCP, Multi-Agent Systems, Evals, Security, and Human Oversight
McLucas, Cameron
Synopsis "Autonomous AI Agents Engineering: Build Reliable Production AI Agents with Python, Planning, Memory, Tools, RAG, MCP, Multi-Agent Systems, Evals, Security, and Human Oversight"
Autonomous AI Agents Engineering: Build Reliable Production AI Agents with Python, Planning, Memory, Tools, RAG, MCP, Multi-Agent Systems, Evals, Security, and Human Oversight Problem Building an AI agent that works in a demo is easy. Building one that remains reliable when it uses tools, preserves state, handles failures, coordinates with other agents, and affects real systems is much harder. If you are a software engineer, AI engineer, technical architect, or developer, you may already know how to call an LLM, add RAG, or connect a few tools. The real challenge begins when the agent must pursue goals across multiple steps without losing control. Poorly engineered agents can repeat actions, use stale memory, exceed permissions, fail silently, lose progress after interruptions, or behave unpredictably when external systems change. Without a solid production architecture, adding more autonomy can simply create more risk, cost, and debugging work. SolutionAutonomous AI Agents Engineering gives you a practical engineering framework for building autonomous AI agents that are bounded, observable, recoverable, secure, and useful in production. You will learn how to move beyond basic model calls and design the complete system around the model: the agent harness, planning loop, tools, memory, RAG, durable execution, permissions, evaluation, security controls, human approval, and production operations. The book shows you how to: Build structured agent loops that observe, plan, act, verify, recover, and stop predictably. Design safe Python tools, sandboxes, typed contracts, runtime budgets, and controlled side effects. Engineer RAG and working, semantic, episodic, and procedural memory with freshness, provenance, and retention controls. Create durable agents that checkpoint state, resume work, handle retries, avoid duplicate actions, and survive interruptions. Connect capabilities with MCP, coordinate multi-agent systems, add evaluations, enforce authority boundaries, and introduce human oversight where consequence requires it. The material is designed for developers who want to build production-ready AI agents rather than one-off experiments. ProofThe book follows a complete production architecture from first principles through deployment. It begins by separating models, harnesses, workflows, tools, and execution environments, then progressively adds planning, memory, recovery, MCP, multi-agent coordination, identity, security, evaluations, observability, and operational governance. Throughout the book, concepts are translated into concrete Python structures, typed schemas, tool contracts, execution patterns, recovery strategies, approval flows, evaluation designs, and production checklists. The appendices reinforce the material with agent patterns, engineering checklists, schemas, security templates, MCP and A2A patterns, deployment guidance, and operational handoff structures. By the end, you will understand not only how autonomous AI agents can act, but how to engineer the surrounding system so those actions remain controlled, inspectable, recoverable, and aligned with real production requirements. If your goal is to build dependable AI agents with Python, RAG, MCP, tools, memory, multi-agent systems, evals, security, and human oversight, this book gives you a practical foundation for doing it with greater confidence and discipline.
