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Anatomy of an Agent – By Stephen Gruppetta

Stephen Gruppetta breaks down the core components of an AI agent, covering its anatomy and how these elements work together in practice using Python.

Background

- Stephen Gruppetta is a Python educator who writes The Python Coding Stack newsletter. This post breaks down what an "agent" is in the context of LLM-based (Large Language Model) software. - The article explains the shift from a simple "LLM call" (send a prompt, get text back) to an autonomous loop (perceive, think, act): the agent has tools (e.g. web search, code executor) and a reasoning engine that decides which tool to call next. - Key concepts: tool-use (giving the model the ability to run external functions), multi-step reasoning (the agent can iterate until a goal is met), and memory (keeping state across steps). This is the architectural difference between a chatbot and an agent. - This matters because "agents" are the dominant paradigm for building useful AI applications in 2024-2025 — they turn LLMs from text generators into autonomous problem-solvers. Frameworks like LangChain, CrewAI, and OpenAI's Assistants API are all built around this anatomy.

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Anatomy of an Agent – By Stephen Gruppetta

Background

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