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.

• 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 production environments

Salvatore Raieli Salvatore Raieli is a senior data scientist in a pharmaceutical company with a focus on using AI for drug discovery against cancer. He has led different multidisciplinary projects with LLMs, agents, NLP, and other AI techniques. He has an MSc in AI and a PhD in immunology and has experience in building neural networks to solve complex problems with large datasets. He enjoys building AI applications for concrete challenges that can lead to societal benefits. In his spare time, he writes on his popularization blog on AI (on Medium).

Gabriele Iuculano Gabriele Iuculano boasts extensive expertise in embedded systems and AI. Leading a team as the test platform architect, Gabriele has been instrumental in architecting a sophisticated simulation system that underpins a cutting-edge test automation platform. He is committed to integrating AI-driven solutions, focusing on predictive maintenance systems to anticipate needs and prevent downtimes. He obtained his MSc in AI from the University of Leeds, demonstrating expertise in leveraging AI for system efficiencies. Gabriele aims to revolutionize current business through the power of new disruptive technologies such as AI.

• AI Agents in Practice, First Edition
• Generative AI with LangChain, Second Edition