What Are AI Agents and How Can Businesses Use Them?

“AI agent” has become one of those terms that means something slightly different in every conversation. A software vendor uses it to describe a support chatbot. A developer uses it to describe a script that calls a language model in a loop. A business owner hears it and reasonably wonders whether it is a new category of tool or a new name for something they already have.

The useful definition is narrower than the marketing one. An AI agent is a system that receives a goal, decides which steps to take, uses tools to take those steps, and checks its own progress until the goal is met or it needs help. The important word is decides. A traditional automation follows a path you drew in advance. An agent chooses the path at runtime based on what it finds.

How an agent differs from a chatbot

A chatbot answers. An agent acts. That is the shortest version, but the practical differences matter more.

	Chatbot	AI agent
Main output	A reply in a conversation	A completed task in a system
Access	Usually just text	Tools, APIs, databases, files
Steps	One turn at a time	Multiple steps, planned as it goes
Success measured by	Was the answer useful?	Did the work actually get done?
Failure mode	An unhelpful answer	An incorrect action in a real system

That last row is the one to sit with. When a chatbot is wrong, someone reads a bad answer. When an agent is wrong, it may have already issued a refund, updated a record, or sent an email. This is not a reason to avoid agents. It is the reason agents need boundaries, logging, and approval steps in places where mistakes are expensive.

What an agent is actually made of

Strip away the terminology and most agents share four parts.

• A model that reads the situation and decides what to do next.
• Tools, which are just functions the model is allowed to call: look up an order, search a document store, create a ticket, send a draft for approval.
• Context, meaning the information the agent is given about the task, your business rules, and what has happened so far.
• A loop with limits, so the agent can take several steps but cannot run forever or wander outside its remit.

Almost all of the engineering effort goes into tools, context, and limits. The model is the part you buy. The rest is what makes it behave like part of your business rather than a general-purpose assistant with opinions.

Where agents fit in a business

Agents earn their keep on work that is repetitive in intent but variable in detail. If every case looks the same, conventional automation is cheaper and more predictable. If every case is genuinely unique and high stakes, a person should handle it. Agents are strongest in the middle.

Some generic shapes that tend to fit:

• Intake and triage. A property maintenance firm receives repair requests by email, web form, and phone notes. Each one needs to be classified, matched to a property and tenant, given a priority, and routed. The intent is always the same. The wording never is.
• Research and preparation. Before a sales call, someone gathers the account history, open tickets, recent invoices, and notes from the last conversation into a short brief. Tedious for a person, well suited to an agent with read access.
• Reconciliation and chasing. A wholesaler compares supplier invoices against purchase orders and delivery notes, flags the mismatches, and drafts the query email for a human to send.
• Document handling. Pulling structured fields out of contracts, quotes, or forms that arrive in a dozen different layouts.
• First-line support with real access. Not a bot that recites help articles, but one that can check an actual order status, then either resolve the case or hand over with the context already assembled.

Notice how many of these end with a draft, a flag, or a handover rather than a final irreversible action. That is deliberate, and it is usually the right place to start.

Deciding whether an agent is the right answer

Before committing, three questions tend to settle it.

Is the task well defined enough to judge? If your team cannot agree on what a good outcome looks like, an agent cannot either. Write down what “done correctly” means first.

Does the agent need information it can reach? Agents are only as good as their access. If the knowledge lives in someone’s head or in a filing cabinet, the first project is getting that information into a usable form, not building the agent. Work involving scattered internal documents often needs a retrieval system that grounds answers in your own content before an agent is worth attempting.

What happens when it is wrong? Some tasks tolerate a small error rate because a person reviews the output anyway. Others do not. Match the level of autonomy to the cost of a mistake, and log every action so you can audit what happened.

A sensible way to start

Pick one workflow, not a platform. Give the agent read access before write access. Run it alongside your existing process for a while and compare. Keep a person in the approval seat until the logs give you a reason to remove them. Then expand.

The teams that get value from agents are rarely the ones that started with the most ambitious plan. They are the ones who picked a narrow, annoying, well understood piece of work and did it properly.

If you are weighing up whether a particular workflow suits an agent, conventional automation, or neither, it is usually worth talking it through before any code is written. We are happy to be a sounding board, even if the answer turns out to be that you do not need one.