What Is an AI Agent? Definition, Types, and How They Work

An AI agent is software that perceives its environment, decides what to do, and takes real actions to reach a goal, usually across several steps and with some autonomy. The difference from a chatbot comes down to one word: action. A chatbot answers your question. An agent goes and does the thing.

What an AI agent actually does

An agent runs a loop. It looks at where things stand, reasons about the goal, picks an action, takes it, checks the result, and goes again until it's done or stuck. That loop is the whole trick. It's what lets an agent book a meeting, update a CRM record, or dig an answer out of four different systems instead of just telling you how you might do that yourself.

Three things separate a real agent from a fancy autocomplete:

• Tool use. It calls APIs, searches the web, sends messages. The good ones can even log into a website with no API and click through it like a person.
• Planning. It breaks a goal into steps and reroutes when a step fails, rather than giving up at the first error.
• Memory. It carries context across turns and sessions, so you're not re-explaining yourself every morning.

Chatbot vs AI assistant vs AI agent

People use these words interchangeably, but they're really points on a line from "talks" to "does."

Capability	Chatbot	AI assistant	AI agent
Responds to questions	Yes	Yes	Yes
Remembers past context	Rarely	Sometimes	Yes
Uses external tools	No	Limited	Yes
Takes multi-step actions	No	Some	Yes
Works toward a goal autonomously	No	No	Yes
Acts inside your real tools	No	Sometimes	Yes

An AI assistant lands in the middle. It's helpful and remembers what you told it, but it mostly waits for the next instruction. An agent is built to take a goal and run with it.

Levels of autonomy

Autonomy isn't on/off. Think of it as a dial:

• Suggest — the agent proposes; you do it.
• Draft — the agent writes the email or fills the form, then waits for your yes.
• Act with review — the agent executes, but anything risky pauses for a human.
• Act autonomously — the agent finishes routine work end to end inside set limits.

Most agents worth trusting in production sit in the draft and act-with-review bands. That's where you get speed without handing over the keys. Save full autonomy for the boring, low-stakes, hard-to-screw-up tasks.

What makes an AI agent reliable

Raw capability is the easy part now. The hard part is capability you can actually trust. Reliable agents tend to share four traits:

• Persistent memory, so context and preferences survive between sessions.
• Guardrails and policy that decide what the agent is even allowed to touch before it acts.
• Human approval on sends, writes, and anything you can't take back.
• An audit trail of every tool call, source, and approval, so you can see exactly what it did.

Strip these out and an agent that "takes actions" is mostly a liability waiting to happen. Build them in and you've got something you can hand real work to.

Risks to manage

Agents fail in specific, predictable ways. They act on a wrong assumption, call the wrong tool, leak data between systems, or burn money looping on a task they can't finish. The fixes are the reliability traits above, plus tight scope. Give the agent a narrow job, make it ask before consequential steps, and keep a trace you can read after the fact. Autonomous enough to help, boxed in enough to stay safe.

How this relates to an AI colleague

An AI colleague is just an AI agent framed as a teammate. Same underlying tech, but it lives where your team already works, holds onto context, and runs under shared rules instead of sitting off in its own app. Next to a generic AI executive assistant, the colleague framing leans harder on durable memory and accountability than on knocking out one-off tasks.

Arlo as a governed AI agent

Arlo is an AI agent you talk to like a coworker, in iMessage, SMS, Slack, Microsoft Teams, and on live phone calls. It connects to Gmail, Notion, Linear, GitHub, and 3,000+ tools, and for anything without an API, it logs in once through a secure browser session and works the site like a person. It keeps persistent memory and sends you a morning briefing. Governance comes standard: every connection clears policy before a run, drafts and sends wait on a reviewer you pick, and every tool call, source, and approval lands in a full audit trace. Founders, realtors, and sales teams use it to hand off actual work, not just questions. The Team plan is usage-based; the Business plan is custom, with admin policy, roles, SSO, and audit export.

Try Arlo

Want an agent that acts, with guardrails already in place? Start with Arlo and put one governed AI colleague to work across the tools your team already uses. More in our resources.