The Anatomy of an LLM Agent: Tools, Memory, and Long-Horizon Execution
A clear, practical explanation of how LLM agents work: treating the LLM as the agent's “brain,” tool calls as actions, and executed tools as the environment. The talk walks through the mechanics of function calling, prompts, schemas, and iterative tool-use loops. It covers strategies for reliable tool calling and the engineering needed for long tool-call chains, including caching, finite-state control, external memory, and techniques to prevent goal drift. Overall, it bridges classic RL concepts with modern LLM agent design to show how to build robust, long-horizon AI agents.Register for the hackathon: https://www.wemakedevs.org/hackathons/assemblehack25 Receive SMS online on sms24.me
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