What is an AI agent?

An AI agent acts, observes results and decides what to do next. Learn how agentic AI works, where human oversight fits, and why reliable workflows start with one bounded job.

Agentic AI is not a magic trick. It is a disciplined way to make machines work towards clear business goals, one bounded task at a time. At its core, the loop is simple: observe the situation, decide what to do next, and act. Then watch what happens, adjust, and try again.

That loop is not new. What has changed is our ability to use language models to interpret goals, synthesise observations, and take concrete actions across tools, applications, and real systems. The surrounding software, permissions, and boundaries are what turn a clever model into a trustworthy working partner.

Let me be precise about what we mean by an ‘agent’.

An agent is a system where a language model can choose an action, observe the result, and then choose its next action. Software around the model provides tools, returns results, tracks the task, and limits what it can do. That distinction matters.

In the lab, a model might generate a plan. In a deployed workflow, the surrounding stack executes that plan, feeds back the result, and lets the model revise its approach. The difference between thinking aloud and doing something in the world is significant. It marks the boundary between a clever predictor and a usable agent.

The useful question is ownership: which decisions does the model own, and which need a human or another system to verify them before action is taken? A practical test is what happens when new information arrives halfway through a task. Who decides what comes next?

For me, that is the difference between agency and autonomy. Agency is about capability: a system can take action. Autonomy is about permission and control: what it can do without human oversight and where it must stop for approval. A system might answer a routine shipping query on its own, for example, while a refund, contract change, or other high-stakes action still needs a person to review it.

In the early days, people chased grand visions of one system that could handle anything end to end. Early deployments have pointed to a more tempered and more useful reality.

Start with a bounded job: a task you can clearly define as complete in the real world.

Give the model one well-scoped objective, a small toolkit, and a clear measure of success. Then let it operate under supervision. If it succeeds, you have a proof point. If it stumbles, you learn what to constrain or refine.

Visibility matters. You need to see where the workflow breaks, what data the model needed, and what information it did not need to complete the task correctly. That visibility makes agentic workflows easier to improve, scale, and trust.

Bringing this into the business is a matter of practice.

Consider a shipping-inquiry agent. It looks up records, checks for mismatches, then either answers the customer or escalates the case. Or a coding agent that makes a change in a sandbox, runs tests, and presents a proposed fix for human review. Or a document-review agent that reads terms, flags anomalies, and routes findings to the right owner.

These are bounded, repeatable patterns. They share a common structure:

A defined job
A credible source of facts
A narrow set of actions available to the model
A clear mechanism for verification and handoff
Build the workflow around a useful job, rather than around the number of model calls it can make. It should complete that job reliably and show its working when something goes wrong.

To build these workflows well, you need clear definitions of done, explicit handoffs, and testing that extends beyond the happy path.

If you are starting today, keep the playbook deliberately pragmatic:

Define done in the real environment. Spell out what success looks like, not only what the model produced.
Give the model one clear objective and a few well-chosen tools. A manageable scope reduces risk and improves reliability.
Build in guardrails and human checks. Make exceptions and approval points explicit from the outset.
Test the whole job, not only the happy path. Include cases where information conflicts, tools fail, or the workflow needs to pause and adjust.
Measure outcomes on representative tasks. Look at end-to-end performance, rather than isolated prompts.

This approach gives ambition a route into day-to-day operations.

Browsers, terminals, APIs, and long-running contexts have made more capable workflows possible. The design problem remains the same: what decisions does the model own, and how will you ensure those decisions lead to correct, auditable outcomes?

Answer that clearly and you can move beyond promising demonstrations towards AI-enabled workflows that deliver value in the real world.