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An AI agent is a system that can manage steps toward a goal, use tools and carry a task forward within defined boundaries. A chatbot is mainly a conversational interface: it may simply answer a prompt, or it may front an agent that does more behind the scenes. The useful distinction is what the system does after you ask, not whether you talk to it in a chat window.
What is an AI agent?
OpenAI’s practical guide defines agents as “systems that independently accomplish tasks on your behalf.” In practice, a model helps manage a workflow: it may decide what to do next, use a tool to retrieve information or take an action, assess the result and continue until it reaches a stopping point or returns control.
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This is a practical definition, not a universal industry standard. OpenAI’s API documentation describes an agent as a configured unit combining a model and instructions with optional tools, guardrails, MCP servers, handoffs and structured outputs. Other products may use different architectures.
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How is an agent different from a chatbot?
A basic chatbot produces a response and waits for another prompt. An agent may control a sequence of steps toward a goal. OpenAI draws the distinction this way: “Applications that integrate LLMs but don’t use them to control workflow execution—think simple chatbots, single-turn LLMs, or sentiment classifiers—are not agents.”
That difference is about workflow control, not appearance. A chatbot interface can sit in front of an agent, and a conversational product may include agent-like workflows. Conversely, a chatbot can use a model or a tool without the system controlling a broader task. Tool use alone does not make something an agent.
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| What to compare | Chatbot that only responds | Agent-style system |
|---|---|---|
| Workflow | Generates a reply to the current prompt. | Can manage multiple steps toward a goal. |
| Tools | May not use tools. | May retrieve information or take actions through available tools. |
| What happens next | Typically waits for the user’s next message. | May evaluate results, continue, stop or hand work back. |
| Interface | Often conversational. | May also use a conversational interface; appearance does not establish whether it is an agent. |
What does an agent workflow look like?
A typical workflow combines model decisions with tool execution. The model may choose or evaluate a next step, call a tool when appropriate, and use the result to determine whether to continue. OpenAI’s Agents SDK is one concrete example: a run can loop through model calls, tool execution and possible handoffs until it reaches a stopping point, such as a final answer. This is an example of one runtime, not a requirement that every agent follow the same design.
- Receive a goal: The system is given a task and instructions that define its boundaries.
- Choose a next step: The model may decide whether it needs information, an action or a response.
- Use an available tool: It may retrieve data or act in an external system, subject to its permissions and guardrails.
- Evaluate and continue: It may use the result to take another step, stop, or transfer work to a person or another agent.
- Return control: The workflow ends when the task is complete, it cannot proceed, or a configured stopping or handoff condition is reached.
Does an AI agent need multiple agents?
No. A single agent can manage a task loop using its tools. A multi-agent setup is an optional way to divide responsibilities, not part of the basic definition. OpenAI describes two orchestration patterns:
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- Manager pattern: A primary agent calls specialist agents while retaining responsibility for the final answer.
- Handoff pattern: Control moves to a specialist for a particular branch of work.
Adding agents can make sense when capabilities or responsibilities need to be divided, but it also adds coordination complexity. A larger number of agents is not automatically better.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should you evaluate a product that calls itself an agent?
“Agent” is a label, not a guarantee of accuracy, reliability or safety. The sources describing agent architecture do not establish a universal performance advantage over chatbots. Assess the specific system on the task you intend to use it for, and look for observable behavior:
Quick Recap
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- Workflow control: Does it manage multiple steps, or only generate a reply?
- Tool access: Can it retrieve data, take actions, or both? Which systems can it reach?
- Context and persistence: Does it maintain state across steps or sessions? Runtime designs differ in who manages state.
- Approvals and guardrails: Which actions require human approval, and what can it do without approval?
- Stopping and handoffs: Can it stop on failure, recognize completion or transfer control to a person or specialist?
- Measured performance: Test it on representative tasks and judge the results rather than assuming that an agent label means it will perform well.
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