AI agent

Part of our topic guide on AI Skills for Business.

An AI agent is software built on an AI model that plans and carries out a multi-step task towards a goal, using tools, data or other systems, with limited step-by-step human input. That's different from a chatbot, which answers one prompt at a time and waits for you to direct the next step.

Give a chatbot a question and it gives you an answer. Give an agent a goal — "find the three suppliers with the best on-time delivery this quarter and draft a comparison" — and it breaks that goal into steps, pulls data from the relevant systems, checks its own progress, and comes back with the result. You set the destination; the agent works out the route.

Why it matters

You'll meet this term a lot over the next few years, and the hype around it moves faster than most people's understanding of what it actually does. That gap matters commercially: most AI and data initiatives don't fail because the technology is missing, they fail because nobody in the business builds the judgement to point an agent at the right problem, check its work, and know when to step in. An agent is only as useful as the person setting it up and reading its output — which is a capability question, not a tooling question.

How it works

An AI agent typically combines a few components:

• A goal or instruction — what you want done, set by a person.
• A planning step — the agent breaks the goal into smaller tasks and decides an order.
• Tools and access — it can call other software, search data, run calculations, or trigger actions in systems it's connected to.
• A feedback loop — it checks whether each step worked and adjusts before moving to the next one.
• A handback point — well-designed agents flag when a decision needs a human, rather than acting on everything unsupervised.

The "agentic" part is the planning and feedback loop. A tool that just answers a single question, however well, isn't acting as an agent — it's the multi-step, semi-independent working-towards-a-goal that earns the name.

Common questions

What AI agent skills do people in non-technical roles actually need?

You don't need to build agents to work well alongside them — you need to write a clear goal and the right constraints, check the agent's output before it's used or shared, and know when a task is too judgement-heavy to hand over at all. In our experience, the people who get the most value from agents are the ones who already understand the task well enough to spot when the agent has got it wrong — the skill sits in the person, not just the tool. That's a data-and-AI literacy question as much as a technical one, which is why we treat it as foundational rather than something only "technical" roles need.