What Are AI Agents? How They Work in a Business

What an AI agent is: a plain-language definition

The U.S. Government Accountability Office describes AI agents with a simple pattern: they sense (collect data), process (use models and rules to decide the next step), and act (take action toward a goal). GAO also draws the key line between agents and generative AI: a generative AI system can answer an order-status question, while an agent can work with other software to actually process the return or exchange.

OpenAI’s practical guide to building agents defines agents as “systems that independently accomplish tasks on your behalf,” built from three core parts: a model that does the reasoning, tools it can use to take action, and instructions — the explicit guidelines and guardrails that define how it behaves. Anthropic, in its guide to building effective agents, describes agents as systems that “dynamically direct their own processes and tool usage.”

The point that matters most for a business: agents are built and bounded by people. They work inside permissions someone set, pursue goals someone defined, and stop where someone decided they should stop. They’re software that can take multistep action under supervision, not independent hires. If you’re newer to the terms, start with what generative AI is.

The five building blocks of a business agent

Use these five elements as your checklist for any agent. If one is undefined, the agent either can’t work or has too much room to roam.

The agent loop: observe, plan, act, check, adapt

What separates an agent from a single AI answer is repetition with feedback. It observes the situation, plans a step, acts with a tool, checks the result, and adjusts the next step. This pattern is often called a ReAct loop, after a 2022 research paper that combined reasoning and acting in language models. A chatbot reply doesn’t check its own output and keep going; an agent does, which is how it carries a task from start to finish.

AI agents vs. chatbots vs. fixed workflows vs. RPA

As HubSpot puts it, “AI chatbots are built for conversations”; agents and chatbots both use AI but differ in their ability to execute. For more on how fixed rules and AI fit together, see what AI automation is. If a bot only needs to answer questions, start with our guide to what an AI chatbot is and its uses beyond support.

Why the difference matters for risk: a chatbot can embarrass you with a bad answer. An agent with write access can change a customer record, send an unauthorized refund, or email the wrong person. RPA is predictable because it never leaves its script; an agent is more flexible, so it needs tighter guardrails, not looser ones.

How an AI agent works in a business, step by step

Walkthrough: an overdue-invoice follow-up agent

A hypothetical example, built from the five building blocks.

The setup. Goal: resolve invoices 30+ days overdue. Tools: read access to accounts receivable, the ability to send email, write notes in the CRM, and post to a Slack escalation channel. Permissions: it can send reminders and log notes. It cannot change payment terms, waive fees, or approve write-offs under any circumstances.

1. The agent flags an invoice that just crossed 30 days.
2. It checks the payment history and confirms the contact on file.
3. It drafts a reminder with the invoice number, amount, and due date.
4. Because a reminder is a low-risk, pre-approved action, it sends it on its own.
5. The customer replies disputing the amount.
6. The agent recognizes a dispute is outside its permissions.
7. It routes the case to an accounts-receivable specialist with the email thread, payment history, and a recommended next step.
8. The specialist approves, edits, or rejects the recommendation.

Step 6 isn’t luck. It’s a stopping condition, defined in advance, doing exactly what it was built to do.

When something goes wrong

Help me write a one-page spec for an AI agent before I build or buy one.

The task: [e.g., follow up on invoices more than 30 days overdue]
Systems involved: [e.g., accounting software, email, CRM, Slack]

Fill in these five sections:
1. Goal: one specific, measurable outcome
2. Tools: each system the agent needs, and why
3. Permissions: for each tool, read-only, draft-only, or write, and what data it can see
4. Actions: what the agent may do on its own vs. what needs approval
5. Stopping conditions: every situation where it must stop and hand off to a person (disputes, dollar limits, unknown contacts, low confidence, repeated failures)

Then list what should be logged for each action, and three tests I should run before going live.

Where businesses are using AI agents today

Each area needs a different level of oversight, from spot checks to approval on every action.

GAO also points to operations as an opportunity: an agent could manage inventory, staff schedules, and equipment status to schedule maintenance automatically. For consumer-facing examples of agent controls, see how Walmart is setting spending limits for AI shopping agents, and for one built for small business owners, Claude Duet.

Supervision: why AI agents aren’t autonomous employees

Despite “digital workforce” marketing, a business agent plans and acts inside human-defined guardrails, not independently of them. An agent has no accountability, judgment under ambiguity, or legal responsibility; the business that deploys it does. Treat autonomy as a dial, not a switch:

OpenAI’s guide names the two classic triggers for handing control back to a person: when the agent exceeds failure thresholds (for example, repeated attempts to understand a request) and before high-risk actions that are sensitive, irreversible, or financially significant. It also suggests rating each tool’s risk by whether it reads or writes, whether its actions can be reversed, and its financial impact.

Every deployment should be able to answer: Who authorized this action? What data did the agent use? Which policy version applied? Who approved any exception? And is there a way to pause the agent immediately if it misbehaves?

What AI agents cost, and how to measure them

There’s no reliable industry-wide price for an agent, because deployments vary so much. Budget for these drivers, and see our guide to AI costs for current tool pricing:

• Model and API usage, billed per call or per token
• Integrations with your CRM, accounting, ticketing, and other systems
• An orchestration platform, or the cost of a custom build
• Monitoring, logging, and security
• The time people spend reviewing and handling escalations
• Testing and periodic audits

Measure value with operating metrics rather than a single number: task completion and first-pass success, escalation rate, cost per completed case, rework and error rate, time saved, and permission violations caught.

Risks and failure modes

How to implement AI agents responsibly: an 11-step checklist

1. Choose a bounded, measurable process, not a vague goal.
2. Define success, failure, and escalation conditions in advance.
3. Map the process, data sources, and system dependencies.
4. Grant least-privilege access only.
5. Start in draft-and-recommend mode, separating recommendation from execution.
6. Add fixed rules for limits, eligibility, and compliance checks.
7. Test with ambiguous inputs, bad data, malicious content, and system outages.
8. Log every input, decision, tool call, and approval.
9. Monitor quality, cost, and override rates continuously.
10. Build a clear path to a person for escalations.
11. Expand autonomy gradually, only after a reliable track record.

For the broader process design around an agent, see our guide to AI workflow automation.

Common questions

A note on risk: Results from AI agents vary by process, data quality, and oversight, and no savings should be assumed in advance. Before expanding an agent’s authority over payments, personal data, or regulated decisions, consult qualified finance, IT, and legal professionals.

Business Prompted guides are written for practical use and checked against the sources listed above. Read our editorial policy.