A Serious (and hype-less) Study Guide on Agents and LLMs
A curated set of resources for understanding LLM agent architecture, the control plane, and how to build effective agents, with direct links to every resource.
Why it matters
This guide offers a hype-free, in-depth look at the state-of-the-art in LLM agent research and development, which is a crucial area for advancing AI capabilities.
Key Points
- 1Recommended path for understanding LLM agents in a few hours
- 2Foundational essays on building effective agents and LLM-powered autonomous agents
- 3Overview of key agent patterns and techniques from original research papers
- 4Introduction to protocols and specifications for the agent control plane
Details
This article provides a comprehensive study guide on large language model (LLM) agents, covering the key concepts, architectures, and techniques for building effective agents. It recommends a curated set of resources, starting with practical overviews from Anthropic and Lilian Weng, followed by introductions to the Model Context Protocol and Claude Code documentation. The article also summarizes seminal research papers on agent patterns like ReAct, Reflexion, Toolformer, and others. The focus is on providing a solid technical foundation for understanding the control plane, tool use, and iterative improvement in LLM-powered autonomous agents.
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