What is an AI agent?

A chatbot answers a question and stops there. An agent takes a goal, splits it into steps and works through them in order. In one line: one of them talks, the other one does the job.

The second difference is tool use. A chatbot usually has nothing but text in front of it. An agent connects to a search tool, a database, a calendar or a mailbox, and it calls those tools as it goes.

The third difference is the stopping rule. A chatbot finishes when the answer is written. An agent stops when the goal is met, when its budget runs out or when a step fails. Those conditions are written down first.

A tool is a defined function the agent is allowed to call. Each one gets a name, an input and an output, and the agent reads what a tool does from that definition.

The working loop is nearly always the same. The agent reads the goal, picks the next step, calls a tool, weighs what comes back and takes another step if one is needed. The loop runs until the goal is met or a stopping rule fires.

Permissions matter here. An agent calls only the tools it was given and cannot reach a system outside that list. Read access and write access are granted separately, because reading a record and changing it carry different risks.

An answer comes from two places. The first is what the model knows in general. The second is the documents and records the tools bring back. In a business setup the second one counts as the real source.

Grounding means something specific: you can see which document an answer came from. The agent writes the source next to the answer, so the reader can check it. Where a question has no source, the right behaviour is to say so rather than invent one.

The source list is agreed during the build. Internal documents, product details, the pricing frame and common questions go on it. If the list is never refreshed, the agent repeats old information, so the update schedule is part of the build as well.

An agent's permissions are never open-ended. Sending, paying, deleting and anything that reaches a customer goes to an approval queue, and the step waits there. Who approved it, when, and on what grounds all go into the record.

The log is the second gate. It holds the goal that was given, the tools that were called, the sources that were read and the result that came out. Without that log, nobody can tell where a mistake entered.

The third gate is the stopping rule. Limits are set on the number of steps, the running time and the cost. When a limit is passed, the agent stops and says so. Without these three gates an agent does not belong in production.

An agent earns its place where the rule cannot be written in one line but the result can still be checked. Sorting incoming requests, pulling facts out of records into a summary, and drafting a reply are the usual cases.

The limit is just as clear. Where the rule can be written down, workflow automation is cheaper and steadier, and an agent adds nothing. Where the result cannot be checked, or a mistake is expensive, the work stays with a person.

Builds start small. One goal is picked, the tool list is kept short, and the approval queue and the log are switched on from day one. ALTAI Digital builds agents in the client's own account and hands over the permission list in writing.