An AI agent is software that works toward a goal you give it. It plans the steps, uses tools such as email, a spreadsheet or a web search, checks the result, and keeps going until the job is done or it needs your approval. A chatbot answers a question. An agent does a piece of work.
Last updated October 9, 2026.
How does an AI agent work?
Most AI agents run the same loop, whether they sort your inbox or watch a competitor's pricing page.
- Goal. You describe the outcome in plain language: "Every Monday, summarize what changed on these five competitor pages."
- Plan. A language model breaks the goal into steps and decides what to do first.
- Tools. The agent acts through connected tools: web search, your CRM, Google Sheets, email drafts, a calendar. Tools are what separate an agent from a model that can only produce text.
- Memory. The agent keeps context: your instructions, what it did last run, and facts about your business. See how AI agent memory works.
- Feedback loop. After each step the agent looks at the result, then continues, retries or asks you. Your corrections feed the next run.
The loop is what makes an agent useful on real work. A single prompt gives you one answer. A loop lets the software handle a task with many steps, and fix its own mistakes along the way.
AI agent vs chatbot vs workflow automation
These three get mixed up, and the difference decides which one you need.
| Chatbot | Workflow automation | AI agent | |
|---|---|---|---|
| Starts from | Your message | A trigger (new row, new email) | A goal, a schedule or a trigger |
| Decides what to do | Replies in text | No. Follows fixed steps you drew | Yes. Chooses steps and tools |
| Handles surprises | Asks you | Fails or skips | Adapts, or stops and asks |
| Acts in other apps | Usually not | Yes, the steps you defined | Yes, within the tools you allow |
| Best for | Questions and drafts | Repeatable, predictable tasks | Work with judgment and many steps |
Use a chatbot when the job ends with an answer. Use workflow automation when the steps never change. Use an agent when the steps depend on what the agent finds along the way. Longer comparisons: AI agent vs chatbot and AI agent vs workflow automation.
Types of AI agents
The classic five
Computer science textbooks, including Russell and Norvig's Artificial Intelligence: A Modern Approach, sort agents by how they decide (overview on Wikipedia).
- Simple reflex agents react to the current input with fixed rules. A thermostat is the usual example.
- Model-based agents keep an internal picture of the world, so they can act on things they cannot see right now.
- Goal-based agents choose actions that move them toward a stated goal.
- Utility-based agents weigh several possible outcomes and pick the one with the best trade-off, such as the cheapest route that is still fast enough.
- Learning agents improve their behavior from experience.
The types you will actually use
Software teams today talk about agents differently, by how much freedom the agent has.
- Assistants work with you in a conversation and act when you ask. A drafting assistant that proposes a reply and waits for your yes is one.
- Autonomous agents run on a schedule or trigger without you present, inside limits you set. A weekly competitor brief is one.
- Multi-agent systems split a job between several agents, each with one role, for example one that finds leads and one that drafts the outreach.
Most business use sits in the first two. Start with an assistant that asks for approval, then give it more freedom as you trust its work.
AI agent examples by team
Concrete jobs agents do today, grouped by team. Browse more in the AI agent examples finder.
- Sales. After a recorded call, an agent writes the follow-up email in your voice, drafts next steps and prepares CRM updates. Nothing sends until you approve. See the Call Follow-Ups template. Another agent scores inbound leads, merges duplicates and flags stuck deals: Lead Pipeline Desk.
- Marketing and SEO. An agent audits your site for SEO, speed and accessibility issues on a monthly schedule (Site Audit), or checks whether AI assistants recommend your brand and who they name instead (AI Search Visibility).
- Strategy and research. An agent tracks competitor pricing, product and hiring pages and briefs you on real changes (Competitor Watch), or runs sourced research with live web search (Researchy).
- Support. An agent finds the answer to a product question in your docs and drafts the reply for you to review (Product Support Inbox Assistant).
- Recruiting. An agent finds candidates that match an open role and are not already in your applicant tracking system (Talent Discovery).
All of these are drafting-and-watching jobs. The agent does the legwork. A person makes the final call.
How to build or run an AI agent
You do not need to write code. The no-code path in AI Agent looks like this:
- Pick a template close to your job, or describe the job in your own words.
- Connect the tools it needs, such as your CRM, inbox or sheet.
- Set the schedule and decide what needs your approval.
- Run it once, read the result, and correct it. The correction improves the next run.
Plans and limits are on the pricing page. If you are new to the topic, AI agents for beginners walks through a first project.
Risks and guardrails
Agents make mistakes, and an agent that can act can make an expensive mistake. Four habits keep that manageable.
- Approval before anything leaves the building. Emails, CRM writes and posts should wait for a yes until the agent has earned trust.
- Scoped tools. Give the agent read access first, and write access only where the job needs it.
- A log you can read. You should be able to see what the agent did and why.
- Small first jobs. Start with a task where a wrong answer costs a few minutes, not a customer.
Details: AI agent guardrails.
Frequently asked questions
What is an AI agent in simple terms?
An AI agent is software you give a goal to, and it figures out the steps, uses tools to do them, and checks its own work. It is closer to a junior assistant than to a search box.
Is ChatGPT an AI agent?
A plain chat conversation is not an agent, because you drive every turn. OpenAI also offers agent-style features that can browse and act on tasks, and those do count as agents. The line is whether the software plans and uses tools to finish a multi-step job, not which brand it carries.
What is the difference between an AI agent and a chatbot?
A chatbot responds to what you type. An agent pursues a goal across several steps and can act in other apps. Many agents include a chat window, which is why the two get confused.
What are examples of AI agents?
A sales follow-up writer, a competitor tracker, a lead scorer, a site auditor, a support reply drafter and a candidate sourcer. The examples by team above link to ready-made versions.
Do I need to code to use an AI agent?
No. In AI Agent you describe the job, connect your tools and set what needs approval.
Are AI agents safe to use at work?
They are as safe as the access and approvals you give them. Keep tools scoped, require approval for outgoing actions, and review the log.
Will AI agents replace jobs?
Agents take over repetitive work such as first drafts, data gathering and routine updates. People still set the goals, review sensitive decisions and handle exceptions. How that balance shifts across the job market is an open question, and nobody can state it with certainty.