The short definition
An AI agent is software that takes a goal and works toward it on its own: it decides what steps to take, uses tools to act in the real world (search the web, call an API, send an email, edit code), observes the result, and repeats until the goal is met or it hits a limit. The large language model is the reasoning core; the agent is everything wrapped around it that lets it act, not just talk.
That last part is the difference that matters. A chatbot answers when you ask. An agent decides what to do next.
Agent vs chatbot vs assistant
- Chatbot / assistant: responds to a prompt with text. You stay in the loop for every step.
- Copilot: suggests an action inline (a code completion, a drafted reply) and you accept it.
- Agent: takes a goal, plans the steps, and executes them across multiple tools, checking its own work along the way.
Most products marketed as "agents" sit somewhere on this spectrum. We tag every tool in the AI Agents directory with where it actually falls, because the honest answer is usually less autonomous than the marketing.
How an AI agent actually works
Under the hood, an agent runs a loop:
- Perceive: read the goal and the current context (a ticket, a codebase, a calendar).
- Plan: decide the next step.
- Act: call a tool (an API, a browser, a function).
- Observe: read the result.
- Repeat until done, then hand back (or take the final action).
Four things make this possible: a capable model, a set of tools it can call (increasingly via open protocols like MCP), memory so it can carry context across steps, and a control loop that decides when to stop. When any of these is weak, the agent fails in ways a demo never shows.
The four levels of autonomy
We classify every agent on a simple ladder, because "how autonomous is it" is the question that decides how much you can trust it:
- Assistant: produces output when asked. No independent action.
- Copilot: suggests; a human accepts and acts.
- Supervised agent: does multi-step work, but a human approves the consequential actions.
- Autonomous agent: acts end to end within guardrails, no per-action approval.
A coding tool like Cursor is a copilot when it autocompletes and a supervised agent when it runs a task. Devin takes a whole ticket and opens a pull request for review. A voice agent that runs an entire phone call is genuinely autonomous. Same word, very different risk.
What agents are good at (and not)
Agents shine on bounded, verifiable tasks: resolving a support ticket, migrating code, researching prospects, drafting content for review. They struggle with open-ended, ambiguous goals where errors compound silently. The practical pattern that works today is supervised: let the agent do the work, keep a human on the consequential decisions.
Where to go next
- Browse agents by what they do: best AI agents for customer support, for sales, for coding.
- Compare two head to head, like Decagon vs Sierra.
- Read what agentic AI means and how it differs from generative AI.