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What is an AI agent — and how does it differ from a chatbot?
Now that "agent" appears in every product description, it is unclear what you are actually buying. In practical terms the difference is single: whether it calls tools itself and executes multiple steps.
vs chatbots Preconditions What not to hand over
The short answer
A chatbot answers questions. An agent takes a goal, breaks it into steps itself, calls tools — search, APIs, files, mail — to execute them, and decides the next step from the result. The difference is not conversational quality. It is execution authority.
That difference changes how you deploy it. When a chatbot is wrong, an answer is wrong. When an agent is wrong, real work is done wrongly. So agent adoption is a question of permission design and undo design far more than model choice.
What actually differs
| Chatbot | AI agent | |
|---|---|---|
| Input | A question | A goal |
| Steps | One response | Multiple steps it decides itself |
| External tools | Rarely any | Search, APIs, files, mail |
| Consequence of failure | A wrong answer | Work executed wrongly |
| Controls required | Output review | Permission scope, approval gates, undo |
| Suitable work | Lookup, drafting, explanation | Structured multi-step processing |
Preconditions — without these three it is too early
What is ready to hand over, and what is not
| Work | Verdict | Why |
|---|---|---|
| Gathering and organising information into a document | Suitable | Reversible, and a person reviews the result |
| Pulling data from several systems into one table | Suitable | Read permissions are enough |
| Drafting first-line replies to routine enquiries | Conditional | Fine with an approval gate before sending |
| Scheduling and setting up meetings | Conditional | External mail only after approval |
| Sending replies to customers directly | Not yet | Irreversible, and the brand risk is large |
| Payments, contracts, HR actions | Not yet | Irreversible, and accountability is unclear |
How to start small
Make the first configuration a read-only agent: it collects information from several systems, organises it, and hands the result to a person. At this stage the logs show you what judgements it actually makes.
Next comes write-with-approval. It produces the draft; a person presses send or save. Record the rejection rate here. If that rate does not fall, there is no basis for moving to autonomous execution.
Autonomous execution comes last, and even then keep the scope narrow — one process, under a threshold, within set hours — and keep watching the logs. Make full automation the first goal and the control design gets pushed to the back.
Frequently asked questions
Would upgrading our chatbot to an agent help?
It depends on the work. If most of it is lookup and explanation, an agent gains you little and adds control overhead. The benefit appears only where there is genuine multi-step work across systems.
Who is accountable when an agent acts wrongly?
Practically, the organisation that granted the permissions. Which is why fixing the permission scope and approval gates in writing before deployment matters. Putting a human gate on irreversible actions is accountability design, not a technical preference.
What about running several agents together?
Consider it only after a single agent runs reliably. Agents calling each other makes tracing a failure much harder. Logging and undo design come first.
Is the cost structure different from a chatbot?
Yes. An agent executes several steps per request, so usage charges grow faster than request counts. Check whether you can cap the number of steps and the monthly usage.
Find where it applies
Start by finding the multi-step work
The automation assessment surfaces the repetitive work that runs across several steps, and how many steps each takes. That is where an agent is worth having.