An Illustrated Guide to AI Agents: Concepts and Code for Building Agents with LLMs, Tools, and Memory

Artificial intelligence is entering a new phase. AI agents can now reason, plan, and act with increasing independence. From accelerating scientific breakthroughs to supporting creative work, these systems are quickly reshaping industries and everyday life. This book provides the conceptual foundation and practical insights you need to understand and effectively work with this emerging technology.

Through hundreds of clear graphic illustrations, Maarten Grootendorst and Jay Alammar explain how AI agents are built, how they think, and where they're heading. Designed for professionals, students, and curious learners alike, this guide goes beyond the buzz to reveal what's actually happening inside these systems, why it matters, and how to apply the knowledge in real-world contexts. An Illustrated Guide to AI Agents is your essential reference for navigating the next frontier of artificial intelligence.

• Explore the core architecture of AI agents: tools, memory, and planning
• Understand reasoning LLMs, multimodal models, and multi-agent collaboration
• Learn advanced methods, including distillation, quantization, and reinforcement learning
• Evaluate real-world applications, strengths, and limitations of AI agents

Maarten Grootendorst is a member of technical staff at Google DeepMind. He holds master's degrees in organizational psychology, clinical psychology, and data science, which he leverages to communicate complex machine learning concepts to a wide audience. With his popular blogs (https://newsletter.maartengrootendorst.com), he has reached millions of readers by explaining the fundamentals of artificial intelligence, often from a psychological point of view. He is the author and maintainer of several open source packages that rely on the strength of large language models, such as BERTopic, PolyFuzz, and KeyBERT. His packages are downloaded millions of times and used by data professionals and organizations worldwide.

Jay Alammar is director and engineering fellow at Cohere (pioneering provider of secure AI for the enterprise). In this role, he conducts machine learning research improving the agentic and tool use abilities of large language models. Through his popular AI/ML blog (https://jalammar.github.io) and Substack (https://newsletter.lan guagemodels.co), Jay has helped millions of researchers and engineers visually understand, use, and build machine learning tools, concepts, and models. Jay is also a co-creator of popular machine learning and natural language processing courses on Deeplearning.ai and Udacity.