What Are AI Agents and What Can They Actually Do?
A plain-English explanation of AI agents, how they differ from chatbots, what they are capable of today, and where they still fail.
An AI agent is software that uses a language model to decide what to do next and then does it, using tools you have given it access to. The important word is "does". A chatbot produces text. An agent takes actions: it looks something up, updates a record, sends a message, books a slot.
Chatbot, assistant, agent
The terms are used loosely, but the distinction is useful.
| What it does | Example | |
|---|---|---|
| Chatbot | Answers questions with text | "Our opening hours are 8 to 5" |
| Assistant | Answers using your information | Looks up your price list and quotes the right figure |
| Agent | Works toward a goal using tools | Checks the calendar, books the appointment, sends a confirmation, updates the CRM |
How an agent works
Strip away the jargon and an agent has four parts:
- A language model. The part that understands the request and reasons about it.
- Instructions. A written description of its job, its limits and how it should behave.
- Tools. Specific actions it is allowed to take: read the calendar, create a contact, send a text, search your documents.
- A loop. It decides on a step, takes it, looks at the result and decides on the next step, until the job is done or it needs to hand over.
Anthropic's engineering team gives a good description of this distinction in their guide to building effective agents, which separates fixed workflows from agents that direct their own process.
What agents do well today
- Conversations with a clear purpose. Booking, rescheduling, answering questions, taking an order.
- Multi-step lookups. "Find this customer, check their last three invoices and tell me what is unpaid."
- Triage. Reading an incoming message and deciding what kind of thing it is and where it should go.
- Working across systems. Reading from one tool and writing to another without a person copying and pasting.
- Handling variation. Unlike a rigid form, an agent copes when a customer says things in an unexpected order.
Where they still fall down
They can be confidently wrong. A language model can state something false with complete assurance. Good design limits this by making the agent answer from supplied information, but it does not remove the risk.
Long tasks drift. The more steps an agent takes on its own, the more chances for a small error to compound.
They do not know what they do not know. An agent will not always recognize that a situation is outside its competence. You have to define the boundaries for it.
They need good tools. An agent that can only read your calendar cannot book anything. Much of the work in building one is connecting it properly to your systems.
What this means for a small business
Useful agents are narrow. "An AI employee that runs the office" is a fantasy. "An agent that answers the phone, books estimates and texts me a summary" works.
A sensible specification covers:
- The job, in one sentence.
- The tools it may use, and nothing more.
- The limits. What it must never do or say.
- The handoff. When it stops and passes to a person, and how.
- The record. A log of what it did, so you can review it.
Questions to ask anyone selling you one
- What exactly can it do, and what can it not do?
- Where does it get its information?
- What happens when it does not know the answer?
- Can I see a record of its conversations and actions?
- How do I change what it says?
If the answers are vague, the product probably is too. For help deciding whether you need an agent or something simpler, see AI agents vs traditional automation.
