AI agent.

An AI agent is a software system designed to pursue a goal by selecting and carrying out actions. It may interpret a request, break the work into steps, use a tool or data source, inspect the result, and decide whether another action is needed. The exact behavior depends on how the agent is built and what it is permitted to do.

The term “agent” covers systems with very different levels of capability. One agent may only suggest the next step for a person to take. Another may perform a limited action, such as creating a draft or updating a record after approval. Calling something an AI agent does not, by itself, establish how autonomous it is, which data it can use, or what actions it can take.

An agent begins with a goal, instructions, and some available context. It uses an AI model to interpret the request and choose a next step. That step might be answering the user, retrieving information, calling a tool, or asking for clarification. If the system receives a result, it can use that result as context for what to do next.

This creates a loop: interpret, act, observe, and decide. The loop may stop when the task is complete, when the agent reaches a limit, or when it needs a person to resolve uncertainty. In many implementations, the agent does not independently control every part of this process. Its actions are constrained by the tools it can access, its instructions, and the permissions assigned to it.

For example, an agent asked to prepare a customer update might retrieve relevant case details, draft a message, and then wait for a person to approve it. Whether it can send the message itself is a separate design and permission decision.

A chatbot is primarily an interface for conversation. It may answer questions or guide a user through a fixed interaction. An AI agent may also communicate through chat, but its defining feature is that it can select actions to advance a goal, sometimes across several steps or tools.

Traditional automation follows rules or a predefined workflow, such as moving a record when a specific condition is met. An AI agent can interpret less structured requests and choose among available actions, but its behavior may be less predictable than a narrowly defined workflow. Systems can also combine these approaches: an agent may use a language model to interpret a request while relying on ordinary automation for tightly controlled operations.

The boundaries are not always clear. Product names and technical implementations vary, so it is more useful to ask what a system can actually do than to rely on the label “agent.”

Agents are most useful when a task involves context, several connected steps, and a clear way to check the result. Potential applications include:

• Gathering information from permitted systems and preparing a summary.
• Sorting or routing incoming requests based on their content.
• Drafting documents, responses, or recommendations for human review.
• Coordinating steps in a workflow, such as collecting required details before an approval.
• Monitoring for a defined condition and notifying a person when it occurs.

These examples do not imply that an agent should make every decision or execute every action without review. A system may be designed to recommend, draft, or prepare work while leaving the final decision to a person.

Suppose an operations team receives internal requests to restore access to business applications. An AI agent could interpret each request, check whether required details are present, consult permitted support information, and suggest a next step. If the request is incomplete, it might ask the employee a follow-up question. If the issue matches a known procedure, it could prepare a response for an analyst to review.

The team could choose to let the agent perform only low-risk actions, such as categorizing a request, while requiring approval before it changes an account or closes a case. This keeps the workflow useful without treating the agent’s interpretation as automatically correct. The appropriate boundary depends on the impact of mistakes and the organization’s ability to monitor and reverse actions.

Before deploying an agent, define its job and boundaries in operational terms:

1. Specify the goal. Describe the task the agent should complete and what counts as a successful result.
2. Limit its tools and data. Provide only the access needed for that task, and consider whether the agent should read information, prepare changes, or execute them.
3. Set approval points. Require human confirmation for actions with significant financial, security, legal, customer, or operational consequences.
4. Test ordinary and unusual cases. Include incomplete requests, contradictory information, unexpected tool results, and attempts to exceed the agent’s scope.
5. Monitor outcomes. Track errors, rejected actions, escalations, and changes made so the team can identify problems and adjust the design.

The amount of oversight should reflect the consequences of failure. A drafting agent may need a different review process from an agent that can alter access, update business records, or trigger external communications.

An agent’s output can be incorrect even when it sounds confident. It may misunderstand a request, rely on incomplete context, select an unsuitable tool, or misinterpret the result of an action. If it can modify systems, an error can affect real data or interrupt a business process.

Important controls include access restrictions, approval gates, clear action logs, testing, and a way to stop or reverse actions where possible. Teams should also decide how the agent handles uncertainty, unavailable tools, conflicting instructions, and requests outside its intended purpose. An agent’s ability to take several steps does not guarantee that those steps are appropriate or that the overall task has been completed correctly.

An AI agent combines interpretation with the ability to take actions toward a goal. That can help with multi-step work, especially when the task has clear boundaries and its results can be checked. But the term describes a broad range of systems, from assistants that only recommend actions to systems permitted to carry them out.

A sound deployment begins with a specific use case, limited access, tested workflows, and oversight proportionate to risk. The goal is not maximum autonomy; it is reliable assistance with clear accountability when the agent succeeds, encounters uncertainty, or makes a mistake.