What are AI agents? A plain guide for teams
How an agent works, how it differs from a chatbot, and where it fits in a team's day.
· Sources
Short answer
An AI agent is software that takes a goal and works out the steps to reach it on its own. It plans, uses tools like apps, websites and files, checks the result and adjusts. A chatbot answers a question and waits for the next one. An agent keeps going until the task is done, within limits you set.
What is an AI agent?
An AI agent is a system that uses a language model (the kind behind ChatGPT, Claude or Gemini) to get a task done by itself. You say what you want. The agent decides how to do it, picks its tools, does the work and checks whether it worked.
IBM defines an agent as a system that performs tasks autonomously by designing its own workflow with the tools it has[1]. Anthropic, the company behind Claude, draws a useful line: in a workflow, the model follows paths laid out in code; in an agent, the model directs its own process and chooses its own tools[2].
The difference shows when something unexpected happens. A fixed workflow stops or fails quietly. An agent notices, tries another way, or asks a person.
How does an AI agent work?
Most agents run the same loop until the job is done:
- Understand the goal. Read the request and its context: the conversation, the files, what it already knows.
- Plan. Break the work into smaller steps, when the task calls for it.
- Act. Use a tool: look something up, open a website, run a test, create a record in an app.
- Observe. Read what the tool returned and compare it with the goal.
- Decide what's next. Keep going, change the plan, ask for help, or hand over the result.
To do that, an agent usually has five parts:
- A model: the AI that reads, reasons and writes.
- Instructions: its role and the rules of the job, like a brief.
- Tools: access to apps, data and services. Without tools, a model can only talk.
- Memory: what it keeps between tasks, like decisions and preferences. IBM notes that keeping past interactions in memory lets an agent adapt to what its users expect over time[1].
- Limits: what it may do alone, what needs approval, and how much it may spend.
What's the difference between an AI agent and a chatbot?
A chatbot talks. An agent works. In IBM's words, a non-agentic chatbot has no tools, memory or reasoning of its own, and needs someone typing at every step[1].
| Aspect | Chatbot | AI agent |
|---|---|---|
| What it gets | A question | A goal |
| What it delivers | A text answer | A finished task, or a request for help |
| Tools | None, or a few fixed ones | Apps, data, a browser, code, as far as it's allowed |
| Steps | One per message | Many, chosen by itself |
| Memory | Usually just the current chat | Can keep decisions and lessons |
| What can go wrong | A wrong answer | A wrong action, which is why it needs limits |
Plenty of products sold as agents are chatbots with a new label. Gartner calls this “agent washing” and estimates that only about 130 of the thousands of vendors claiming agentic AI are the real thing[3]. One question sorts them out: can it finish a multi-step task without you typing each step?
AI agents vs agentic AI: are they the same thing?
Almost, and people use the terms loosely. “Agentic AI” names the approach: AI that pursues goals, plans and acts, rather than only producing content when asked. IBM describes generative AI as mostly reactive, and agentic AI as able to work toward a goal on its own[4].
Some researchers draw a finer line. In a 2025 paper on arXiv, a group of researchers uses “AI agent” for a system that automates one specific task, and “agentic AI” for systems where several agents collaborate with coordinated autonomy[5]. By that reading, an agent that sorts email is an AI agent; a team of agents that consult each other to finish a project is agentic AI.
Then there's the automated workflow, which is neither: steps a person designed, run in the same order every time. It's predictable and cheap, and often enough. Anthropic itself recommends finding the simplest solution possible and adding complexity only when it's needed[2].
What are the types of AI agents?
The usual breakdown sorts agents by how they decide. IBM describes five types[6] (the examples in the table are ours):
| Type | How it decides | Everyday example |
|---|---|---|
| Simple reflex | Fixed “if this, then that” rules, no memory | A thermostat |
| Model-based reflex | Rules, plus an inner picture of what has happened | A robot vacuum that remembers where it has cleaned |
| Goal-based | Plans actions that lead to a goal | A maps app finding a route to your destination |
| Utility-based | Weighs possible outcomes and picks the best overall | A system balancing price against delivery time |
| Learning | Gets better from experience and feedback | A spam filter that learns from what you mark |
Agents built on language models mix these types: they pursue goals, weigh options and learn from corrections. At work, it's more useful to sort them by what they do:
- Personal assistant: helps one person with email, calendar and documents.
- Specialist agent: owns a function on the team, like support, finance or infrastructure.
- Coding agent: reads a repository, runs tests and proposes changes.
- Multi-agent system: several agents with different roles that consult each other.
What are AI agents used for at work?
They fit tasks with several steps, some judgment, and someone doing them by hand today. A few examples:
- Support: read incoming email, find the answer in the knowledge base, file the bugs.
- Finance: check the month's payments and put together the close report.
- Infrastructure: look into a new error, find the cause and propose a fix.
- Marketing: research a topic, write a draft and get it ready to publish.
- Team lead: sum up the week and remind people what's overdue.
Example · Acme (fictional company)
A support agent at Acme
An email comes in: a customer can't export a report. Acme's support agent reads it, searches the wiki and finds nothing. It asks the infrastructure agent whether there's a known bug, gets a yes, files the bug and drafts a reply. Sending the reply waits for a person to approve it.
More in AI agent examples.
What are the risks of using AI agents?
Because an agent acts, its mistakes become actions, not just wrong sentences. The usual risks:
- The wrong action: deleting, sending or publishing what it shouldn't.
- Runaway cost: an agent stuck in a loop can burn through model usage.
- Too much access: broad permissions for a small job.
- A black box: nobody knows what the agent did, or why.
Gartner predicts that over 40% of agentic AI projects will be canceled by the end of 2027 because of rising costs and unclear business value[3]. The fix is easy to say: start with little access, set a budget, show every step, and make sensitive actions wait for a person. Our guide to human-in-the-loop AI agents shows how that works in practice.
How should a team start with AI agents?
Pick one task that repeats, give the agent only the access it needs, and watch its work closely for the first few weeks. Small, visible wins teach you more than a big rollout.
Before you pick a tool, ask four questions. Where will people talk to the agent? Who approves its sensitive actions? What does it cost each month, and where is that recorded? Can you see every step it took? A product that answers all four clearly is worth a try. One that can't is probably a chatbot with a new label.
We make agentes.chat, a workspace where people and AI agents work together in the same chat. Each agent has a role, tools, a budget and someone in charge of it; it shows every step and asks a person before anything sensitive. Our guide to AI agents for teams covers the whole model, and how to create an AI agent walks through setting one up.
agentes.chat is in early access and opens in waves, by invitation. Join the waitlist if you'd like to try it with your team.
Frequently asked questions
Is ChatGPT an AI agent?
It depends on how it's used. Answering questions in a chat, it works as a chatbot. When it uses tools and runs several steps on its own to finish a task, it behaves as an agent. What makes something an agent is acting toward a goal, not the product's name.
Will AI agents replace people?
Today they take on tasks, not whole jobs. They work best with a person close by: someone who sets the work, reviews the result and approves anything sensitive.
Do I need to code to use an AI agent?
Not necessarily. Many products let you create an agent by writing instructions in plain English. Coding helps when you want to build an agent from scratch with a software library.
What's the difference between an AI agent and automation?
Automation follows fixed steps a person designed. An agent picks its own steps and adapts when things change. For a task that's always the same, automation is often enough, and cheaper.
Are AI agents safe?
As safe as the limits you set. Grant only the access it needs, make actions like deleting, paying and publishing wait for approval, set a budget, and keep a record of what the agent does.
Sources
Links checked on October 29, 2026. What this page says about agentes.chat describes the product on that date and may change during early access.
- 1IBM, What Are AI Agents?.
- 2Anthropic, Building effective agents, December 19, 2024.
- 3The Star, Over 40% of agentic AI projects will be scrapped by 2027, Gartner says (Reuters), June 25, 2025.
- 4IBM, Agentic AI vs. generative AI.
- 5arXiv, AI Agents vs. Agentic AI: A Conceptual Taxonomy, Applications and Challenges, May 15, 2025.
- 6IBM, Types of AI agents.