Plain-language explainer
AI agents, explained
What makes an AI agent different from a chatbot?
An agent is a language model placed inside a loop that can take actions. A chatbot writes a reply and stops. An agent proposes a tool call, a harness runs it, the result comes back into the context, and the model decides the next step, repeating until the task is done. The model still only predicts text. The power comes from the loop around it: read a file, run a search, call an API, check the result, try again. That loop is what turns a predictor into something that gets work done.
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What people get wrong
- The model itself reaches out and touches your systems. A separate harness runs the tools; the model only proposes calls.
- An agent is just a longer prompt. The difference is the action loop and tool results re-entering context.
- More autonomy is always better. Useful agents verify their work and ask for approval on risky steps.
Where you see it in real products
- Coding agents read code, run tests, and iterate until they pass.
- Research agents search, read, and compile findings across many steps.
- Ops assistants call internal APIs to look things up and take actions.
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