Anatomy of an Agent: Model, Tools, Memory and a Feedback Loop

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A DEV Community article, part of a seven-day series on agentic engineering, breaks down the core architecture of AI agents into four components: memory, model, tools, and result. The author describes these as connected in a loop, where the model chooses tools, tools produce results, and results feed back into the process. The model is presented as a capability the team cannot easily control, so systems should be designed to swap models without rewrites. The piece recommends routing tasks by difficulty, using small models for simple work and reserving frontier models for complex reasoning. It also argues that well-designed, focused tools matter more than exposing many API endpoints to a model.

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