AI Agent Definition: How Agents Plan and Act

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AI Agent in plain language

Think of an agent as a task worker with a limited toolkit, not simply a better answer box. For a support investigation it might search earlier tickets, inspect a service status and ask for missing details before proposing a resolution.

A chat window can contain an agent, and an agent can run without a chat window. The useful question is whether the model chooses subsequent actions from observations, rather than whether the interface looks conversational.

How it works

The visual guide

The action–feedback loop

Within the assigned goal and permissions

Choose
Select the next useful action from the current task state.
Act
The application runs an allowed tool or requests approval.
Observe
Read the actual result, including errors or missing information.

If work remains, return to Choose with the new result. Stop on completion, a limit or a blocker.

A conceptual loop, not a promise of full autonomy. A tool result is evidence of what ran; the model saying “done” is not.

Define a finish line
Specify an observable result: a draft diagnosis with supporting ticket IDs, for example, rather than “fix support”.
Give a small, clear toolkit
Separate read operations from changes. Define what a successful response, a denied request and a tool error look like.
Track progress and stop
Record actions and results. End when the task is complete, information is unavailable or the permitted time or step budget is exhausted.

Illustrative example · Fictional data

Investigating a fictional login ticket

Input: A user reports that sign-in fails after a password reset. Available tools can read service status and search approved support articles; they cannot change accounts.
Process: Check service status. If there is no incident, find reset-session guidance and ask whether the user has tried a fresh session. Keep the ticket open if the result is unknown.
Expected output: A draft reply linking the relevant article, plus the observation that no incident was returned by the status tool.
How to check it: Reject a claim that the password was reset by the agent: no write tool exists. A useful test also makes the status tool fail and checks that the agent reports uncertainty.

Where it helps

Investigating requests where the next lookup depends on an earlier result.
Coding assistance that reads a failure, changes a file and checks a test.
Research tasks that require several source searches and a supported synthesis.

Agent, chatbot or automation?

Chatbot: A conversational interface. It may generate one response or contain an agent behind the scenes.
Fixed automation: Code defines the route in advance. Prefer it when the necessary steps and exceptions are already clear.
AI agent: Selects subsequent actions within a bounded task. Useful when observations change the route.
Multi-agent system: Several agents with separate responsibilities pass work between them — for example one gathers sources, one drafts and one checks. Adds coordination and handoff cost, so justify it against one agent with a larger toolkit.

Common pitfalls

A tool timeout can be mistaken for a completed action unless the application checks results.
Giving an agent more tools also increases the number of choices and failure paths to test.
Persistent memory is not automatic; decide what state to store, for how long and for whom.

Put the idea to work

Try it yourself

Write a three-tool support toolkit on paper. Walk through one normal ticket and one failed lookup. Mark the exact point where the agent must ask a person instead of inventing the missing result.

Common questions

Does an AI agent need multiple models?

No. One model can choose actions across several steps. Add more models or agents only when the division of work improves the task. Extra models add cost, latency and more places for errors to hide, so start with one capable model and split the work only when tests show a clear benefit.

What is a multi-agent system?

A design in which several agents hold separate responsibilities and pass work between them — one searches sources, one drafts, one reviews against the brief — instead of one agent doing everything with a larger toolkit. The separation can make each part easier to describe, test and restrict: a reviewing agent needs no write access, and a drafting agent needs no database credentials. The costs are real too. Work has to be handed over, and context is lost or distorted at each handover; a mistake made early can be inherited rather than caught; and the failures are harder to reproduce because the route differs between runs. Before splitting the work, write down which responsibility each agent holds and what evidence passes between them. If that list reads like one job, one agent is usually the better design.

Does every agent need RAG?

No. Retrieval may be a useful tool, but an agent could instead work with a calculator, test runner or another source of observations. Give an agent the tools its task actually needs. A coding agent may rely on a test runner, while a research agent may depend on search and retrieval.

Can agents work without approval at every step?

Yes, within permissions established by the application. Read-only work and consequential changes should have different approval rules. A common pattern lets the agent read, search and draft freely, but requires a person to approve payments, deletions, external messages and other changes that are hard to undo.

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