An AI agent is a software system that uses an AI model to choose steps toward a goal, often by calling connected tools, checking what happens, and deciding what to do next. A basic chatbot usually returns an answer; an agent can direct part of a multi-step workflow. Its actions are limited by its tools, instructions and permissions, and it may need human approval or help.
What makes software an AI agent?
The defining feature is not simply that a product uses a large language model (LLM). It is that the model has some control over the workflow: it can select a next step based on the goal and the current state, rather than only generating a response or following a fixed sequence of steps.
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OpenAI’s practical guide describes three core elements: a model that makes decisions, tools it can use, and instructions that set its behavior and guardrails. Implementations may also include a runtime, memory, data connections, orchestration, handoffs or structured outputs. Those are design choices, not a universal checklist. [OpenAI’s practical guide to building agents]
Definitions differ among providers. Anthropic, for example, defines an agent as an AI model that directs its own processes and tool use rather than following a fixed script. Google Cloud distinguishes agents from assistants and bots, while recognizing that assistants can include agent-like capabilities under user supervision. There is no single boundary every vendor uses. [Anthropic’s explanation of trustworthy agents in practice] [Google Cloud’s overview of AI agents]
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How do AI agents actually work?
A useful mental model is a repeating reason–act–observe cycle. The model does not have unlimited reach or human-like understanding; it selects among the steps and tools made available to it, then uses the resulting information to guide what happens next.
- Receive a goal and context. The system gets a task, relevant information and instructions that define what it may do.
- Choose a next step. The model decides whether it can respond directly, needs more information, should call a tool, or should ask a person.
- Use an available tool, if needed. A tool may retrieve data, change a record, send a message or pass work to another agent.
- Inspect the result. The agent receives the tool’s output and uses it to decide whether to continue, change course, stop or request human input.
- Finish or hand control back. The process ends when the task is complete, the system reaches a stopping condition, or it cannot proceed safely or confidently.
For example, an expense agent might extract a receipt’s vendor and amount, categorize the expense, consult an available policy and submit it through a connected expense system. If the expense exceeds a limit or the policy is unclear, the agent may need to ask the employee before proceeding. That example illustrates a possible workflow; not every agent has access to expense software or handles every case correctly. [Anthropic’s explanation of trustworthy agents in practice]
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How are agents different from chatbots and fixed workflows?
A chatbot can be useful without being an agent. A simple chatbot, a single-turn LLM application or a sentiment classifier may use an LLM but not let the model control how the workflow proceeds. In an agent, the model has some discretion over which step or tool to use in response to the task and what it observes. [OpenAI’s practical guide to building agents]
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1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problems| Type of system | How the workflow proceeds | What it may do |
|---|---|---|
| Basic chatbot | Usually returns a response to a prompt; it need not direct a larger workflow. | Answer a question or generate text. |
| Fixed workflow | Follows a sequence explicitly defined in code. | Carry out predictable steps in the order its developer specified. |
| AI agent | The model chooses among available steps based on the goal and current state. | Use tools, inspect results, continue, change course or ask for help. |
The boundaries can overlap in products: an assistant may include agent-like features but keep a person closely involved. The practical question is not the label on the product. It is whether the model directs parts of the workflow, what it is allowed to do, and when control returns to a person. [Google Cloud’s overview of AI agents]
What can an AI agent do for you?
An agent can only take actions exposed through its connected tools. OpenAI groups tools into three broad categories:
- Data tools retrieve information the agent needs, such as records or documents.
- Action tools change something outside the model, such as updating a record, sending a message or handing off a ticket.
- Orchestration tools let one agent use another agent as a capability.
Instructions and guardrails shape how tools are used, but they do not give the system access it does not have. An agent with no email-sending tool cannot send an email; one with a connected tool may still be limited by its account permissions or approval rules. [OpenAI’s practical guide to building agents]
Illustrative workflows include deciding whether a customer-service refund meets policy, reviewing vendor-security documents, handling insurance-claim paperwork or submitting a receipt. These examples show where agents might be applied, not proof that a particular system will make those decisions accurately or produce a business benefit. [OpenAI’s practical guide to building agents] [Anthropic’s explanation of trustworthy agents in practice]
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When is an agent a good fit?
Agents are worth considering when a workflow is difficult to express as a reliable set of rigid rules—for example, when it involves judgment calls, exceptions, complex rules or information buried in unstructured documents. Those traits make a workflow a candidate for agentic automation, not a guarantee that an agent will work well or save time or money.
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- Consider an agent when the system must interpret varied inputs, make contextual choices and adapt the next step to what it finds.
- Prefer a fixed workflow when the steps are predictable and a deterministic implementation can handle them. OpenAI recommends validating that an agent is actually needed rather than adding one by default. [OpenAI’s practical guide to building agents]
What are the limitations and risks?
An agent can make a poor decision, encounter misleading tool output, use an allowed capability in an unsuitable way or reach an exception it cannot resolve. The fact that it can act across multiple steps does not mean it is dependable without supervision.
Responsible designs can use human approvals, check-ins, explicit stop conditions, guardrails, error handling and monitoring. Developers can inspect execution traces and evaluate the system before and after deployment. These measures help people oversee behavior; they do not guarantee that every answer or action is correct. [OpenAI’s practical guide to building agents] [Anthropic’s explanation of trustworthy agents in practice] [Google Cloud’s core concepts of AI agents]
For production systems, security and operations matter alongside the model. Google Cloud highlights a secure runtime, identity and access controls, error handling, monitoring and traces, plus evaluation before and after deployment. OpenAI recommends setting an evaluation baseline before optimizing for model cost or latency. Choosing a larger model alone is not evidence of reliability. [Google Cloud’s core concepts of AI agents] [OpenAI’s practical guide to building agents]
How to assess an agent before using or building one
For a buyer or builder, evaluate the implementation against representative tasks and the consequences of mistakes—not just its demo or the “agent” label. Useful questions include:
- Does it perform reliably on the cases and exceptions the workflow actually contains?
- Which data sources and action tools can it access, and what permissions govern them?
- Can a person approve sensitive actions, take over a task or make the agent stop?
- What guardrails, identity controls and error-handling behavior are in place?
- Can operators evaluate outputs, monitor runs and inspect traces when something goes wrong?
- Does its output format work with downstream systems, and what runtime, cost and latency trade-offs apply?
For systems built with OpenAI’s Agents SDK, the documented configuration can combine a model and instructions with optional tools, guardrails, MCP servers, handoffs and structured outputs. OpenAI recommends beginning with a focused agent and splitting responsibilities when capabilities, tools, approval policies, models or output styles materially differ. [OpenAI API agent definitions]
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