Agent, or just a model with a nice interface?
The word is applied to almost anything at the moment, so it is worth being precise. An AI agent has three properties that a chatbot does not.
- It pursues a goal, not a reply. The instruction is an outcome — reconcile this invoice — rather than a question to answer.
- It uses tools. It can query a database, call an API, read a file, send an email. Without tools, it can only talk about doing things.
- It runs in a loop. Act, observe the result, decide the next step, repeat until done or stuck.
Take that loop away and you have a chatbot. We compare the two in AI agents versus chatbots.
How the architecture fits together
- The goal. A request from a person, or a trigger such as an incoming email.
- The model. Decides what to do next given the goal and everything observed so far.
- The tools. A defined set of actions the agent is permitted to take.
- The memory. What has happened in this run, plus retrieved reference material, usually through RAG.
- The guardrails. Limits on what may be done without a human, a cap on steps, and a log of every action.
The engineering effort is almost entirely in tools, memory and guardrails. Making those safe, observable and reversible is ordinary software integration, which is why it lands with AI integration.
What an agent looks like in practice
Invoice reconciliation
An invoice arrives by email. The agent extracts the supplier, reference and line items, finds the matching purchase order, compares them, and either posts the match or raises an exception. Clean matches stop being touched by a person at all.
First-line support triage
A ticket arrives. The agent classifies it, looks up the customer, answers directly if the question is one of the routine forty, and otherwise routes it with a summary attached.
Internal knowledge assistant with actions
A member of staff asks about leave entitlement. The agent answers from current policy, and — because it has tools — can also submit the request.
When an agent is the right answer
An agent earns its extra cost when the task needs several steps whose order depends on what is found along the way. If the sequence is fixed, conventional workflow automation will be cheaper and easier to debug.
What agents cost
A single-purpose agent with two or three tools and a human approving anything consequential is typically a three-to-six-month build. The cost drivers are the number of tools, the consequence of an action, the quality of the source systems, evaluation, and running cost — a reasoning loop makes several model calls per task, not one.
Start read-only. Let it draft the action and have a person approve it. Widen its authority only where you have evidence it is reliable. For the wider decision, see AI for business.