09-15-2026, 01:22 PM
An Illustrated Guide to AI Agents by Maarten Grootendorst and Jay Alammar is a practical, intuition-first introduction to how modern AI agents work. Rather than treating an agent as simply an LLM with a prompt, the book explains an agent as a system that combines an LLM with capabilities such as reasoning, memory, tools, planning, reflection, and interaction with its environment.
It begins with the foundations of LLMs—including transformers, self-attention, mixture-of-experts models, and test-time reasoning—and then builds toward more sophisticated agent architectures. The authors use extensive visual explanations and a progressively developed “TinyAgent” to make the underlying mechanisms concrete.
The later chapters focus on the components that make agents genuinely autonomous: short- and long-term memory, retrieval-augmented generation, tool calling, tool learning, Model Context Protocol (MCP), planning, task decomposition, reflection, and evaluation. It also covers multimodal agents, coding agents, multi-agent collaboration, and techniques such as distillation, quantization, supervised fine-tuning, and reinforcement learning.
Overall, the book is positioned as both a conceptual guide and a hands-on technical reference for understanding and building agentic AI systems, particularly useful for readers who want to move beyond using LLMs toward actually designing AI agents.
BOOK
It begins with the foundations of LLMs—including transformers, self-attention, mixture-of-experts models, and test-time reasoning—and then builds toward more sophisticated agent architectures. The authors use extensive visual explanations and a progressively developed “TinyAgent” to make the underlying mechanisms concrete.
The later chapters focus on the components that make agents genuinely autonomous: short- and long-term memory, retrieval-augmented generation, tool calling, tool learning, Model Context Protocol (MCP), planning, task decomposition, reflection, and evaluation. It also covers multimodal agents, coding agents, multi-agent collaboration, and techniques such as distillation, quantization, supervised fine-tuning, and reinforcement learning.
Overall, the book is positioned as both a conceptual guide and a hands-on technical reference for understanding and building agentic AI systems, particularly useful for readers who want to move beyond using LLMs toward actually designing AI agents.
BOOK
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