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Agentic AI Engineering: Design, Build, and Operate Reliable AI Agents with Tools, Memory, RAG, MCP, and Multi-Agent Systems
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Publisher: Independently Published
Pages: 260
Weight: 1.01
Height: 0.55 Width: 7.00 Depth: 10.00
Language: English
Building an AI agent that can call a tool is easy. Building one that can operate reliably in production is not.
Agentic AI Engineering is a practical engineering guide to designing, building, and operating production-grade AI agents. Instead of treating agents as prompts wrapped around an LLM, this book develops them as controlled software systems in which models participate in decision-making while deterministic infrastructure remains responsible for execution, permissions, state, safety, and recovery.
Starting from a minimal agent runtime and evolving a recurring system called OpsPilot, the book covers the architecture required to move from experimentation to production.
You will learn how to:
- distinguish LLM applications, workflows, agentic workflows, and true agents
- design bounded agent execution loops and durable runtimes
- implement structured tool and function calling safely
- engineer state, context, short-term memory, and long-term memory
- build retrieval and agentic RAG systems with provenance and evidence controls
- use planning, decomposition, reflection, and verification appropriately
- introduce human approvals and durable checkpoints
- design long-running and event-driven agents
- work with Model Context Protocol (MCP)
- design multi-agent systems and agent-to-agent communication
- defend against prompt injection, confused-deputy risks, and unsafe tool execution
- apply sandboxing, least privilege, identity, and authorization
- engineer retries, idempotency, reconciliation, and failure recovery
- trace and evaluate agent behavior in production
- control inference cost, latency, concurrency, and scaling
- move stable behavior back into deterministic software when appropriate
The examples emphasize framework-independent architecture first, then show how those principles map onto modern agent infrastructure.
This book is written for Applied AI engineers, GenAI and LLM engineers, backend engineers, ML engineers, software architects, platform engineers, senior Python developers, and technical founders building real AI systems.
If you want to understand what exists underneath agent frameworks, and how to turn probabilistic model decisions into controlled production software, this book is for you.
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