What is an AI agent? A practical business guide

AI agent definition: a goal, tools and controlled action

An AI agent is an AI system that receives a goal and carries out steps to achieve it using tools. The goal defines the outcome. Instructions set the boundaries. Tools let the system retrieve information or take action. This distinguishes an agent from a model used only to generate text.

For example, an agent could receive the goal of preparing a customer follow-up. It might read an approved customer record, identify missing information, draft a message and ask for permission before sending it. The workflow depends on its tools and access.

Autonomy does not mean unrestricted authority. A useful business definition includes permissions, stopping conditions and human oversight. An agent should know what it may do, what requires approval and when to hand the task back.

How do AI agents work?

AI agents work through a loop: interpret the goal, choose an action, use a tool, inspect the result and decide what comes next. The loop can stop when the task is complete, approval is needed or the agent cannot proceed safely. A business implementation should define these stopping conditions explicitly.

• Model: interprets information and proposes the next step. Its output still needs checking.
• Tools: provide permitted access to information or actions, such as reading records or updating a spreadsheet.
• Memory: retains relevant task context or previous results. Stored information should have clear access and retention rules.
• Instructions: define the goal, acceptable methods, boundaries and situations that require a handover.
• Approvals: keep sensitive actions under human control, such as sending a message or changing an important record.

Types of AI agents in business

AI agents can be grouped by the work they are intended to perform. These categories overlap: a customer-service agent might use a workflow tool, while a digital worker might support several departments. Choose by the task, information access and authority needed, rather than the label alone.

• Assistant agents help people find information, prepare drafts and organise work, with the person directing the task.
• Customer-service agents handle customer questions and defined service processes, with routes for human escalation.
• Coding agents carry out software-development tasks within a controlled development environment.
• Workflow or back-office agents coordinate administrative steps, records and handovers across a process.
• Computer-use agents, also called digital workers in this context, operate software interfaces to carry out tasks.

12 AI agent examples by department

These AI agent examples are possible use cases, not claims about any named product. Each needs suitable tools, approved information and clear limits. Start with preparation or checking before allowing an agent to make consequential changes.

• Sales: prepare account summaries from approved CRM records before a meeting.
• Sales: draft follow-up emails for a salesperson to review and send.
• Customer support: answer questions using approved help material and escalate uncertainty.
• Customer support: collect missing case details before handing over to staff.
• Marketing: draft campaign briefs from approved product information and audience notes.
• Marketing: check draft content against a brand and claims checklist.
• Finance: flag invoice discrepancies for review without authorising payments or corrections.
• Finance: prepare expense summaries from permitted records for a finance reviewer.
• HR: answer routine policy questions using approved documents and escalate exceptions.
• HR: prepare onboarding task lists without making employment or access decisions.
• IT: gather issue details and suggest troubleshooting steps within approved procedures.
• Operations: update a task tracker after confirming the required supporting information.

AI agent vs chatbot vs automation

An AI agent, a chatbot and automation are not interchangeable terms. A chatbot is a conversational interface. Automation is work carried out by a system rather than manually. An agent uses tools to pursue a goal and responds to results. A chatbot can contain an agent, and an agent can use fixed automation.

The practical distinction is not whether the system has a chat window. Ask what it can change, how it chooses its next action and where approval is required. See AI agent vs chatbot for a focused comparison.

Examples of AI agent products, grouped by purpose

This selection is grouped by type, not ranked. It covers workplace assistance, coding, customer service and digital workers. Inclusion does not establish suitability for a particular workflow. Check access requirements, controls and task fit before choosing.

The descriptions differ in scope: some name a work product, while others describe an agent or an agent platform. Compare the work you need done, not just the category.

• Workplace assistance — OpenAI ChatGPT Work: ChatGPT for projects, tasks and teams.
• Coding — Anthropic Claude Code: an agentic coding tool.
• Coding — Cognition Devin: described by Cognition as an autonomous software engineer.
• Customer service — Intercom Fin: a customer agent.
• Employee and customer work — Salesforce Agentforce: a platform for autonomous AI agents in the Salesforce ecosystem.
• Digital workers — Terabot (our product), by AITG (our company): AI agents that each get their own computer; available in private preview by invitation.

What should an AI agent be allowed to do?

Give an AI agent the minimum access needed for its task. Separate reading information, preparing an action and executing it. These are different permissions. An agent that can draft a customer response does not automatically need permission to send it or change the customer record.

Define an approval policy before deployment. It could require review for payments, deletion, external messages and changes to sensitive records. Also define what happens when information is incomplete, instructions conflict or a tool returns an unexpected result.

Keep a record of actions and make it possible to stop the agent. Test exception handling, not just successful tasks. Human oversight is most useful when the reviewer can see what the agent used, what action it suggests and why that action needs attention.

How to start with an AI agent

Start with a bounded task that has a clear completion condition. Document the inputs, permitted tools, expected output and approval points. Preparing a reviewed summary is easier to define than an open-ended instruction to manage an entire department.

Run the agent with representative work and compare its output against a human-reviewed result. Include missing information, conflicting records and tool failures. Decide how errors will be corrected and who owns the process before expanding access. Assess usefulness by completed work and review effort, not by how convincing the responses sound.

If the next step is an agent that does the work in existing applications, consider Terabot (our product). Its digital workers each use a private virtual machine. Businesses can request an invitation to the private preview.

Choosing an approach for global and Malaysian teams

For global and Malaysian teams, start with the same practical questions: what work should be completed, which information is required and who can approve changes? Assess the deployment against your organisation's language needs, access policies, data-handling requirements and existing applications. Do not assume a product fits because it uses the term agent.

Ask providers to demonstrate the actual workflow, including an exception and a human handover. Review where information is processed and stored, how permissions are managed and how actions can be audited. Treat these as checks to complete, not capabilities to assume.

If your workflow centres on operating application interfaces, read what is a digital worker. It explains the concept behind a worker-style approach. Choose the implementation around the process and its risks, rather than a broad technology label.