Building LLM Agents with RAG, Knowledge Graphs & Reflection A Practical Guide to Building Intelligent, Context-Aware, and Self-Improving AI Agents By Mira S. Devlin Transform Large Language Models into Intelligent Agents That Reason, Retrieve, and Reflect Large language models can generate text-but intelligence requires more than words. True intelligence demands reasoning, memory, and reflection. It requires systems that can connect what they know, retrieve what they need, and learn from what they produce. In Building LLM Agents with RAG, Knowledge Graphs & Reflection, AI systems architect Mira S. Devlin guides you beyond the surface of generative AI into the world of agentic intelligence-where LLMs evolve from reactive tools into dynamic collaborators capable of grounding responses in truth, understanding context, and improving over time. This book doesn't just explain concepts-it helps you build them. Each chapter blends theory, diagrams, and applied examples to show how retrieval, reasoning, and reflection interact inside modern AI agents. Whether you're constructing a self-updating research assistant or a multi-agent workflow, you'll gain a deep understanding of how today's most advanced cognitive systems are designed. What You'll Learn The Cognitive Core of AI Agents Understand the architecture of transformers, tokenization, and attention. Explore the shift from static LLMs to adaptive, outcome-driven agents. Learn how retrieval, reflection, and reasoning form the four pillars of intelligence. Retrieval-Augmented Generation (RAG) Master the techniques that make models factually grounded and transparent. Implement retrievers, rankers, and generators using open-source frameworks. Evaluate accuracy with metrics like Recall@K, Precision@K, and grounding quality. Knowledge Graphs and Structured Reasoning Design and query graph-based knowledge systems using Neo4j, ArangoDB, or GraphRAG. Combine structured knowledge with unstructured language for explainable AI. Reflection and Cognitive Loops Build agents that evaluate their own outputs and correct themselves. Implement Plan → Act → Reflect → Revise cycles for self-improving intelligence. Explore short-term and long-term memory systems for continuous learning. Multi-Agent Collaboration Use frameworks like CrewAI, LangGraph, and AutoGPT2 to orchestrate coordination. Key Features End-to-end coverage: From LLM fundamentals to advanced RAG and reflection architectures. Practical code labs: Step-by-step walkthroughs in Python with modular components. Visual clarity: Concept diagrams, data flow maps, and evaluation schematics throughout. Debugging insights: Identify hallucinations, reasoning gaps, and retrieval errors with real-world examples. Scalable design patterns: Extend single-agent models into multi-agent collaborative systems. This book is written for: AI developers, data scientists, and engineers who want to move beyond simple LLM prompts. Architects and product innovators building intelligent, explainable, and adaptive AI systems. Researchers and students seeking a structured understanding of retrieval-based reasoning and reflection. Tech leaders and educators integrating agentic AI into enterprise or academic environments. You don't need a supercomputer-just intermediate Python skills, a working knowledge of APIs, and curiosity. Every example can be run on a standard laptop or cloud environment. Order Now.