What is an AI agent? A plain guide to what they do and what they cost

An AI agent is software that takes a goal, decides the steps itself, and uses other software to carry them out. A chatbot answers; an agent acts. The difference is not the model behind it. It is whether the thing can take actions in the world, check the result, and try again when it fails.

That distinction is doing a lot of work in 2026, because almost every vendor now calls its product an agent. This guide covers what actually makes something an agent, how one works, where the line sits against ordinary chatbots, and what they cost. Our catalogue carries 126 active products in the AI agent category, so there is a real field to look at once the vocabulary is clear.

What separates an agent from a chatbot

Three capabilities, and a product needs all three to earn the name:

• It plans. You give an outcome, not a sequence. The agent works out the steps.
• It uses other software. Search, a database, your calendar, an API, a browser. Without the ability to call something outside itself, it can only talk.
• It closes the loop. It observes what happened, notices when a step failed, and adjusts. This is the one most products marketed as agents cannot actually do.

A chatbot with a search function is still a chatbot: the human decides every step. An agent given “reconcile last month’s invoices against the bank feed and flag anything unmatched” chooses its own path, and tells you what it found.

How an AI agent works

Under the marketing, almost every agent runs the same loop:

1. It receives a goal in plain language.
2. A language model plans the next step, using whatever context it has about your data and the actions available.
3. It calls a function. That means anything it has been given access to: query a database, send an email, read a file, hit an API.
4. It reads the result and decides whether the goal is met.
5. It repeats from step two until done, blocked, or stopped by a limit you set.

Two things in that loop decide whether an agent is useful or dangerous. The first is what it is allowed to call, because an agent can only do what you gave it access to, which makes permissions the real design decision. The second is when it stops, because a loop with no ceiling can run up a bill or repeat a mistake many times before anyone notices.

Agentic AI and AI agents: the same thing?

Near enough, and the difference is only grammatical. An AI agent is a specific piece of software. Agentic AI is the broader description of systems built to act rather than to answer. Vendors use the second term when they want to describe a capability across a product rather than one named feature.

Treat neither word as a specification. When a vendor says “agentic”, ask the three questions from the first section: can it plan, what can it call, and how does it know it failed.

AI agents by job, and the products in each

Agents are bought by what they are allowed to touch, so that is how this list is grouped. Each product below sits in the category named on its row in our catalogue.

Job	What it is given access to	Products we list
Coding agents	Your repository: reads and edits across many files	Claude Code, OpenAI Codex, Devin, Factory AI, Augment Code, Amp, OpenHands, Zed
Agent builders	Nothing on their own; the platform you assemble an agent on	Dust, Relevance AI, Lyzr, Eden AI, ZBrain, Shakudo, Pickaxe, Inworld, Project Bonsai
Customer-facing agents	Help docs, order records, the ticket queue	Ribbo, Watermelon, Klart AI, AskMyDocs

Three honest caveats on that table, because they change how much weight to give it.

It is grouped, not ranked. Of the 126 active products in our AI agent category, 3 carry user reviews and none carries a SpotScore, so there is no defensible order to put them in. Anyone presenting a ranked top ten of AI agents in 2026 is ranking marketing.

Coding agents are the most mature row and the easiest to evaluate, because the output is a diff you can read. Our AI coding agent category holds the full set, and Windsurf vs Cursor and Cursor vs GitHub Copilot compare the editor-native ones directly.

Builders are not agents. They are the platform you build one on, which matters for budgeting: you are buying a development surface and then paying separately for the model usage underneath. Our AI agent builder category holds 26 of them.

Two jobs deliberately have no row. Internal operations agents, which reconcile records and move data between systems, are where most of the measurable return sits and where the least is published, so we hold no clean set to name. Research agents overlap almost entirely with assistants rather than agents, and are covered in what is an LLM.

For a longer worked list by business function, our AI agent use cases post covers 24 enterprise examples.

Is ChatGPT an AI agent?

Partly, and the honest answer is that it depends which feature you mean.

Used as a chat window, it is an assistant: you drive every step. Used with its browsing, code execution and connected apps, it does plan and call software, which meets the first two tests. What it generally does not do is run unattended against a goal for an extended period with its own error handling, which is the third.

So: agentic features inside an assistant, not an agent you would hand a whole process to. We list the vendor as OpenAI in our large language models category, which is the layer underneath all of this.

How much do AI agents cost?

Three pricing shapes, and they are not comparable:

• Per seat, like ordinary software. Predictable, and the easiest to get approved.
• Per action or per token, where you pay for what the agent consumed. This is where bills surprise people, because an agent that loops ten times costs ten times as much as one that succeeds first try.
• Quote only. Common at the enterprise end. In our AI agent category, most listings record quote-based pricing instead of a published figure.

The cost nobody budgets for is evaluation. An agent that acts needs someone checking that it still acts correctly after a model update. That is a person’s time, not a licence fee, and it is usually the larger number.

For the procurement view, including security questions worth asking before a pilot, read Enterprise AI agents.

Before you buy one

Four questions that separate a real agent from a renamed chatbot:

1. What can it call, and who approved that list? Access is the whole risk surface.
2. What happens when a step fails? If the answer is “it tells the user”, it is an assistant.
3. Where does it stop? Ask for the limit, in actions or in spend.
4. How will you know it broke? Agents fail quietly. Decide who watches, before you switch it on.

Frequently asked questions

Related reading

• AI agent use cases: 24 enterprise examples by function.
• Enterprise AI agents: platforms, pricing models and security.
• What is an LLM?: the layer every agent is built on.
• Claude Pro price: what the assistant subscriptions underneath cost.