Building Intelligent AI Agents: Building Autonomous AI Systems with LLMs, Tools, RAG, MCP and Agentic Workflows
AI agents are moving beyond simple chatbots.
Modern AI systems can reason about goals, retrieve knowledge, use external tools, maintain memory, plan multi-step tasks, collaborate with other agents, and take controlled actions.
But building a production-ready agent requires much more than connecting an LLM to a few APIs.
Building Intelligent AI Agents provides a practical engineering guide to designing autonomous AI systems using:
Large Language Models (LLMs)Tool Use & Function CallingAgent MemoryRetrieval-Augmented Generation (RAG)Planning & ReasoningAgentic WorkflowsMulti-Agent SystemsModel Context Protocol (MCP)Production AI ArchitectureSecurity & GuardrailsAgent EvaluationObservability & MonitoringYou will progressively move from AI agent fundamentals to production-grade autonomous systems.
The book uses the NovaShop scenario throughout the journey, allowing you to understand how individual concepts combine into a complete AI architecture.
You will learn how to build systems that can:
Understand → Plan → Retrieve → Decide → Act → Observe → Verify → Recover
The final capstone brings everything together into an end-to-end autonomous AI agent incorporating LLMs, tools, memory, RAG, planning, workflows, multi-agent coordination, MCP, security, guardrails, evaluation, observability, human approval, failure recovery, and cost optimization.
This book is designed for AI Engineers, ML Engineers, Data Engineers, Software Engineers, GenAI developers, and developers preparing for AI engineering roles.
The goal is not simply to build an agent that can think.
The goal is to build an AI system that can think, act, verify, recover, scale, and remain trustworthy in the real world.
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