Building AI Agents with LLMs, RAG, and Knowledge Graphs: A Practical Guide to Autonomous and Modern AI Agents by Salvatore Raieli and Gabriele Iuculano

 

📘 Overview

Released in July 2025 via Packt Publishing, this 560‑page guide covers the full pipeline of agent-oriented AI—from fundamentals of LLMs, to RAG architectures, to knowledge graph integration, all culminating in runnable, production-grade autonomous agents 

The book targets data scientists and ML engineers with Python experience who want to build real-world agents capable of reasoning, acting, and grounding responses in reliable data 

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🧭 What the Book Covers

1. Foundations of LLMs, RAG, and Knowledge Graphs

Introduces how LLMs serve as the “brain” of agents, then explains building RAG pipelines to retrieve external knowledge, and layering on knowledge graphs to structure context and reasoning 

2. Practical Agent Architectures

Detailed Web‑based code examples (mainly Python) using frameworks like LangChain, showing how to build multi-agent orchestration, planning logic, memory structures, and tool-based execution flows 

3. Grounding for Reliability

Highlights techniques to reduce hallucinations such as proper retrieval augmentation, source attribution, prompt design, and knowledge graph grounding—reflecting best practices in RAG systems 

4. Deployment, Monitoring & Scaling

Guidance on how to move agents from experimentation to production—including deployment patterns, orchestration, observability, logging, and release strategies in enterprise settings 

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🧠 What Reviewers & Practitioners Say

• Malhar Deshpande—who served as technical reviewer—calls it “the most practical and forward‑looking resource available right now” for RAG pipelines, knowledge graphs, and multi-agent orchestration 

• Alex Wang distinguishes it as the “practical and more advanced” complement to broader systems‑level books, praised for its code, architecture diagrams, and focus on grounded reasoning workflows 

✅ Strengths

• Extremely practical: Includes runnable code, architecture diagrams, and real-world use‑cases rather than abstract theory.

• Modern coverage: Fully integrates RAG and knowledge graph methods, reflecting the current best practices to enhance factual robustness.

• Hands‑on multi-agent orchestration: Shows how agents interact, plan, remember, and execute tasks via tool integrations.

• Enterprise‑grade approach: Offers advice on deployability, observability, and scaling in production environments.

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⚠️ Limitations

• Steep learning curve: Tailored more to practitioners; readers unfamiliar with Python or basic ML tooling may find sections dense.

• Less emphasis on ethics and governance: While the book is grounded in engineering best practices, strategic concerns like transparency, bias, trust (TRiSM) are not its central focus—a contrast with companion resources that tackle ethics explicitly 

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🔍 For Whom It’s Ideal

• Data scientists, ML engineers, and AI developers building useful, grounded agents in industry settings.

• Teams wanting a hands-on guide to implement RAG + knowledge graph pipelines and orchestrate agents for real-world automation.

• Anyone curious about building autonomous, tool-enabled agents that reason, retrieve and act—without resorting to pre‑built platforms.

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🧩 How to Use It in Practice

If you're building, say, an agent for document-based decision support:

1. Use the sample code for indexing & embedding your documents using LangChain or similar frameworks.

2. Construct a knowledge graph to model entities and relations for retrieval-driven reasoning.

3. Design agent workflows, combining plan‑generate‑act cycles equipped with APIs/tools.

4. Add guardrails, observability, and prompt hygiene to reduce risk of hallucination or misuse.

5. Deploy and monitor agents in production using logging, health checks, and performance metrics.

This is exactly the pipeline Raieli and Iuculano walk through in detail 

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🏁 Final Verdict

Building AI Agents with LLMs, RAG, and Knowledge Graphs is a comprehensive, implementation-first manual for modern AI agent builders. Packed with code, architecture patterns, and real‑world advice, it equips engineers to go from theory to production‑ready agents. While not focused heavily on ethics or strategic systems thinking, its value lies in its clarity, practicality, and up‑to‑date techniques.

If you want to build reliable, autonomous AI agents—grounded in external knowledge and capable of acting via tools—this book stands out as a strong foundation and companion to broader strategic resources.