What is an AI agent, really?
The word is everywhere. Here is a plain definition — and what turns a clever demo into a useful AI teammate.
An agent is defined by its job, its tools and its reviewer — not by how impressive it sounds.
The word describes everything and nothing.
Ask five software vendors what an AI agent is and you will receive five different answers. The word now covers everything from a chat window with a few buttons to software that plans and acts for hours on its own. For a team trying to decide whether an agent belongs in its workflow, that breadth is the problem.
The definition we build by is simpler: an AI agent is software that carries out a defined job on a person’s behalf. It uses connected tools and business information, follows working instructions for that role and hands its result to someone who can check it. Agentic AI is the broader term for this kind of capability; an AI agent is that capability applied to one defined job.
The difference between an agent and a chatbot is not intelligence. A chatbot answers a question and the conversation moves on. An agent completes work: it can use several tools in sequence, keep going when the answer is not immediate and prepare something a colleague can use — a draft, an update, a summary, a proposed correction. That handover, not the model behind it, is what makes the software an agent.
Take this with youIf you cannot name the job, the tools and the reviewer, you do not yet have an agent — you have a demo.
From a model to an AI teammate
Define the job
Name one repeatable task and what a useful result looks like.
Under the hood
Keep purpose, working instructions and the expected output in the role definition so the work is assessable.
Connect tools and context
Choose the tools and information the job needs, through your own accounts.
Under the hood
Bind connections, knowledge and permissions to the role. Grant only what the job requires.
Work and review
The agent prepares the result; consequential actions wait for a named person.
Under the hood
Separate preparation from sending, publishing or changing official records. Define review at the consequential step.
Inspect and adjust
The owner checks activity, corrections and cost, then updates the role.
Under the hood
Record activity, tool use and outcomes so the owner can refine instructions and boundaries from evidence.
Give the definition somewhere to live.
In the Command Centre, the definition becomes a setup a business owner can work through: the purpose and working instructions, the tools and information the role may use, and the review points around consequential actions. Each part answers one of the questions in the definition.
The instructions say what good work looks like for this job. The connections determine what the agent can actually do, using your own business accounts. The review points decide which actions wait for a person. Taken together, they turn a general AI model into a role the team can discuss, inspect and adjust.
This also gives business and technical owners a shared language. The business owner describes the job and what a useful result looks like; the technical owner decides which tools and information can support it. Both are working toward the same observable handover.
Under the hood +
Keep the role definition separate from the underlying model. The role holds the purpose, instructions, allowed connections and review points; the model is an operating choice that can change without rewriting the job.
Start narrower than feels necessary.
A narrow agent is easier to trust and easier to improve. Prepare a weekly update, sort a support inbox, draft a reply for review — one repeatable job with a result the owner can check. Broad ambitions are hard to evaluate; a defined task either produces a usable result or shows you exactly what is missing.
Give the agent the smallest set of tools that completes the job. More access is not more capability; it is more to review. The instructions, connections and review points should be explainable to the person who owns the role in one short conversation.
The test is whether the person receiving the result can act on it. If the owner keeps reopening every conversation to verify the draft, the agent has moved the work, not removed it. Judge an agent by the usefulness of its handover — and adjust the role from what the activity record shows.
Take this with youJudge an agent by the usefulness of its handover, not by the fluency of its answers.
What is the definition of an AI agent?
An AI agent is software that completes a defined job by using tools and information on a person’s behalf, within boundaries a named owner can review. In practice, that means a role with working instructions, selected connections, review points at consequential actions and a named owner — not simply a chat window.
What is an agent in AI?
In AI, an agent is a system that can perceive information, decide what to do and act toward a goal, often by using tools. An AI agent applies that idea to business work: it follows role instructions, uses connected tools and information, and hands a result to a person who reviews it.
How do AI agents work?
An agent works through a loop: it takes the job and its working instructions, gathers the information it needs, decides the next step, acts through connected tools and prepares a result for review. A well-run setup makes each step visible, so the owner can see what was done, which tools were used and where anything stopped.
How is an AI agent different from a chatbot?
A chatbot answers a question in a conversation. An agent completes a defined job: it can use tools and business information in sequence, prepare a result and hand it over for review. The difference is the job and the handover, not the intelligence of the model.
What is agentic AI, and how is it different from an AI agent?
Agentic AI is the umbrella term for AI systems that act toward a goal — planning steps, using tools and adapting to what they find — rather than only responding to prompts. An AI agent is that capability applied to one defined job, with instructions, connected tools, review points and a named owner.
What are examples of AI agents in business work?
Common examples are repeatable tasks with a checkable result: preparing weekly updates, sorting a support inbox, drafting replies for review, summarising documents or assembling information for a decision. The best first job repeats often, has accessible inputs and a named reviewer.
What types of AI agents are there?
AI research groups agents by how they decide, from simple rule-following agents to systems that learn from feedback. In business software, the practical distinction is between a single-task agent that prepares one defined job and a multi-step agent that carries a workflow through several connected tools. Either way, start with one narrow, checkable job.
Can an AI agent work without supervision?
An agent can be left to prepare routine work, but someone should always own the role and the review points around consequential actions such as sending, publishing, changing official records or spending money. Ownership and review are part of the definition, not an optional extra.
What is an AI teammate in UseGlic?
An AI teammate is our term for an AI agent configured for a defined business role: working instructions, selected connections, knowledge and review points, owned by a named person in your workspace.