An AI agent uses a model to work toward a goal through repeated steps: it interprets context, chooses an action or tool, evaluates the result, and continues—or asks a person for input. The model does not act on outside systems by itself; the application running it executes tool calls and enforces permissions. “Agent” covers a range of designs, so autonomy, memory, tool access, and human control depend on the particular system.
What is an AI agent?
An AI agent is software that uses an AI model to select steps toward a goal, often by calling tools connected to other systems. A chatbot may answer a question in a single exchange; an agent can use an answer or a tool result to decide what to do next. That distinction is about how a system is built and used, not a universal line separating every chatbot from every agent.
A useful starting model has three parts: a model that interprets context and chooses what to do, tools that provide information or carry out operations, and instructions that define the goal, behavior, and guardrails. The surrounding application may also manage state, run the repeated cycle, validate outputs, log activity, enforce limits, and request approvals. OpenAI describes the model, tools, and instructions as the basic components in its practical guide to building agents.
How does an AI agent work?
The central mechanism is a loop rather than a single answer. OpenAI’s Agents SDK documentation describes calling the current agent’s model, inspecting its output, executing tool calls or handing work to another specialist when applicable, and returning once there is a final answer and no more tool work. Anthropic describes the practical difference from a chatbot as a self-directed cycle of planning, acting, observing, adjusting, and repeating until the task is done or human input is needed. The exact implementation varies by product.
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- Receive a goal and context. A person or application supplies a request, along with relevant instructions and information.
- Choose a next step. The model interprets what it has received and may answer directly, ask a clarifying question, or propose a tool call.
- Check and execute the tool call. The host application or runtime determines whether the call is allowed and runs the connected tool. A model’s request to take an action is not the action itself.
- Observe the result. The tool returns information or reports what happened. The runtime can also handle failures or apply validation, depending on its design.
- Continue, stop, or check in. The model can use the result to choose another step, return a final response, or ask for a person’s input. The workflow may also stop when it reaches a configured limit or other stopping condition.
For example, an agent asked to find an appointment might search available times, inspect the results, and present suitable options. If it is also permitted to book, the runtime may execute a booking tool call; whether the user must approve first depends on that workflow’s permissions and safeguards.
What tools can an agent use?
Tools determine what an agent can access and do. OpenAI groups them into three practical categories:
- Data tools retrieve context, such as information from a database, a PDF, or a web search.
- Action tools change something in a connected system, such as updating a record or sending a message.
- Orchestration tools route work to another agent as part of the workflow.
A search-and-summarize agent has a different practical reach from one allowed to edit customer records, send messages, or initiate payments. The model’s abilities matter, but its connected tools and the runtime’s permissions determine which external operations can actually occur.
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Do AI agents have memory or learn over time?
Not necessarily. Some systems carry conversation history or other state from one step to the next; others may use server-managed state or retain little between runs. Those are implementation choices, not properties that every agent automatically has. OpenAI’s runtime documentation describes different ways to carry history and state forward and warns that combining strategies without reconciling them can duplicate context.
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What makes an AI agent different from a chatbot?
The clearest practical distinction is whether the system can choose and execute successive steps through tools, using what it observes to decide what comes next. A basic chatbot interaction generally centers on producing a response to the current prompt. An agentic workflow may search, call an API, inspect the result, and take another step before responding.
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The boundary is not absolute: chat products may use tools, and agents may require close guidance or do only one bounded task. The label alone does not reveal how much autonomy a product has, whether it changes external data, or whether a person must approve actions.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Can an AI agent take actions on its own?
Some workflows can proceed with little intervention, while others require a person to approve or guide each step. Autonomy is a spectrum, not a yes-or-no feature—and greater autonomy is not automatically better. The meaningful questions are what the agent is allowed to do, what triggers approval, and whether a person can see, pause, or stop the run.
The MIT AI Agent Index’s 2025 paper, published in the FAccT ’26 proceedings, reviewed a defined sample of 30 deployed systems. In that sample, 20 of 30 documented pause or stop mechanisms, and 5 of 30 offered watch modes for real-time oversight. Those counts describe the index’s selected systems, not every agent on the market. The index also emphasizes that its sample includes different autonomy levels.
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OpenAI’s practical guide says, “Actions that are sensitive, irreversible, or have high stakes should trigger human oversight until confidence in the agent’s reliability grows.” That is particularly relevant when a tool can spend money, change important records, disclose sensitive information, or send a message that cannot easily be recalled. Anthropic’s published principles for trustworthy agents likewise emphasize human control, alignment with human values, secure interactions, transparency, and privacy; these are Anthropic’s stated principles, not a universal certification standard.
When does it make sense to use multiple agents?
A single agent with suitable tools and clear instructions can handle a broad range of tasks. OpenAI recommends expanding one agent’s capabilities incrementally because multiple agents add coordination complexity and overhead. They are useful when there is a concrete benefit from dividing work—for example, distinct specialist roles or a workflow that needs clear separation of responsibilities.
Two common patterns are a manager agent that calls specialists as tools and a more decentralized arrangement in which agents hand tasks to peers. Neither architecture is a requirement for something to count as an agent, and adding agents does not by itself make a system more capable or reliable.
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How to evaluate an agent before relying on it
Compare the actual workflow rather than relying on the “agent” label or a claim about autonomy. For an agent connected to consequential systems, check:
- Tool access: Which services and data can it reach?
- Action scope: Are tools read-only, or can they change records, send communications, or spend money?
- Approval rules: Which steps need human confirmation, and can those rules be changed?
- State retention: What context is saved, for how long, and across which runs?
- Oversight: Can a person inspect activity, pause it, or stop it?
- Operational safeguards: What limits, error recovery, validation, and monitoring does the runtime provide?
These questions reveal more than a simple ranking by autonomy. A narrowly permissioned agent with clear approval points may be a better fit for a high-impact workflow than one that can act more freely.
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