AI Agents Explained: How They Actually Work
Originally published on DevToolHub . AI agents explained in one sentence: software where an LLM decides what to do next — which tool to call, with what arguments — based on the result of what it just did, in a loop, instead of following a script you wrote in advance. That loop, and the model's control over it, is the entire difference between an agent and a regular app that happens to call an…
AI agents are software systems where large language models (LLMs) make decisions on their own during runtime, determining which tools to employ and with what parameters, based on the outcomes of their previous actions. This contrasts with regular applications that call LLMs using pre-written scripts. The fundamental distinction between an agent and a standard application is that an agent maintains control over its process and tool usage, whereas a workflow involves predefined code paths where LLMs and tools are orchestrated.
An agent functions dynamically by directing its own processes and tool utilization, as opposed to a workflow where the code decides what happens next.
Written by urgent.news from Dev.to's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
