Foundations
The Agent Loop
The agent loop is the core observe-think-act cycle that drives all agentic behavior: the model receives context, reasons about what to do next, executes a tool or produces output, then feeds the result back into the next iteration. This cycle continues until the agent determines the task is complete, hits a predefined limit, or requires human input, making the termination condition one of the most important design decisions you will encounter. Every other concept on this map, from context engineering to evaluation, is ultimately about making individual iterations of this loop more reliable, efficient, or safe.
connected to
resources
Building Effective Agentsanthropic.comAnthropic's guide to agent architectures including the core loop pattern (anthropic.com)Claude Code Overviewdocs.anthropic.comHow Claude Code implements the agentic loop in practice (docs.anthropic.com)LLM Powered Autonomous Agentslilianweng.github.ioDeep technical breakdown of agent loop components and memory systems (lilianweng.github.io)ReAct: Synergizing Reasoning and Actingarxiv.orgThe foundational paper on combining reasoning and acting in language model agents (arxiv.org)OpenAI Function Calling Guideplatform.openai.comPractical guide to implementing tool-use loops with the OpenAI API (platform.openai.com)