Master LLM fundamentals to advanced techniques like RAG, reinforcement learning, and knowledge graphs to build, deploy, and scale intelligent AI agents that reason, retrieve, and act autonomously Key Features: - Implement RAG and knowledge graphs for advanced problem-solving - Leverage innovative approaches like LangChain to create real-world intelligent systems - Integrate large language models, graph databases, and tool use for next-gen AI solutions - Purchase of the print or Kindle book includes a free PDF eBook Book Description: This AI agents book addresses the challenge of building AI that not only generates text but also grounds its responses in real data and takes action. Authored by AI specialists with deep expertise in drug discovery and systems optimization, this guide empowers you to leverage retrieval-augmented generation (RAG), knowledge graphs, and agent-based architectures to engineer truly intelligent behavior. By combining large language models (LLMs) with up-to-date information retrieval and structured knowledge, you'll create AI agents capable of deeper reasoning and more reliable problem-solving. Inside, you'll find a practical roadmap from concept to implementation. You'll discover how to connect language models with external data via RAG pipelines for increasing factual accuracy and incorporate knowledge graphs for context-rich reasoning. The chapters will help you build and orchestrate autonomous agents that combine planning, tool use, and knowledge retrieval to achieve complex goals. Concrete Python examples built on popular libraries, along with real-world case studies, reinforce each concept and show you how these techniques come together. By the end of this book, you'll be well-equipped to build intelligent AI agents that reason, retrieve, and interact dynamically, empowering you to deploy powerful AI solutions across industries. What You Will Learn: - Learn how LLMs work, their structure, uses, and limits, and design RAG pipelines to link them to external data - Build and query knowledge graphs for structured context and factual grounding - Develop AI agents that plan, reason, and use tools to complete tasks - Integrate LLMs with external APIs and databases to incorporate live data - Apply techniques to minimize hallucinations and ensure accurate outputs - Orchestrate multiple agents to solve complex, multi-step problems - Optimize prompts, memory, and context handling for long-running tasks - Deploy and monitor AI agents in pr