How AI Agents Work: A Technical Breakdown of Model, Tools, Memory, and Control Loop

Unite.AI has published a structured technical explainer on the architecture of AI agents, covering the four core components: the underlying model, the tool-use layer, memory systems, and the control loop that ties them together into autonomous task execution. The piece breaks down how each component interacts — for example, how the control loop decides when to invoke tools versus when to rely on the model's parametric knowledge, and how memory (both in-context and external) shapes multi-step reasoning. For developers new to building agentic systems, this serves as a solid conceptual foundation before diving into frameworks like LangGraph, AutoGen, or custom orchestration logic. Experienced practitioners will find it a useful reference for communicating agentic architecture to stakeholders or new team members. As agent-based application patterns continue to proliferate, a shared vocabulary around these four components is increasingly valuable across teams.