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1Fix the driver behind crashes, sound loss and screen glitches2Clear out junk files and repair common Windows errors3Scan for outdated or missing drivers - takes under a minuteAn AI agent is a system that can manage steps toward a goal, use tools, and carry a task forward within defined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 a conversational interface that may simply answer a prompt. The distinction is what the system does behind the conversation: a chatbot can also front an agent if the system manages a workflow and takes steps to complete it.
What is an AI agent?
OpenAI’s practical guide defines agents as “systems that independently accomplish tasks on your behalf.” In practice, an agent uses a model to help decide what to do next, may call tools to gather information or take an action, and can continue through a workflow until it reaches a stopping point.
This is an operational definition, not a universal industry standard. OpenAI’s API documentation describes an agent as a configured unit that can combine a model and instructions with options such as tools, guardrails, MCP servers, handoffs, and structured outputs. Other products may use different architectures.
How is an AI agent different from a chatbot?
A basic chatbot receives a prompt, generates a response, and waits for the next message. An agent can control what happens after the first response: it may break a task into steps, retrieve relevant information, use an action tool, check progress, and continue or return control.
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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.” A conversational interface alone therefore does not tell you whether a product is an agent. The system behind it may manage a workflow even when the user interacts through chat.
| What to compare | Chatbot that only responds | Agent-style system |
|---|---|---|
| Workflow | Generates a reply to a prompt and waits. | May manage multiple steps toward a goal. |
| Tools | May have no tools or use them only to prepare a response. | May retrieve information or take actions through tools, within its permissions and guardrails. |
| Progress | Usually leaves the next step to the user. | May evaluate results and continue, stop, or hand work back. |
| Interface | Often conversational. | Can also use a conversational interface; appearance does not establish the underlying workflow. |
How do agent workflows work?
A typical agent workflow alternates between model decisions and tool use. The model may choose a next step; a tool can then supply data or perform an action in another system. The model can evaluate the result and decide whether to continue. Tool definitions and guardrails help constrain what the system can do, but tool access is not by itself proof that a product controls a workflow.
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OpenAI’s Agents SDK describes one implementation in which a run loops through model calls, tool execution, and possible handoffs until it reaches a stopping point, such as a final answer. That is an example of an agent runtime, not a requirement for every agent.
Does an AI agent need multiple agents?
No. A single agent can manage a task loop using its tools. A multi-agent setup is useful when work benefits from dividing responsibilities or capabilities, but it adds coordination and is not part of the basic definition.
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- Manager pattern: A primary agent calls specialist agents while keeping responsibility for the final answer.
- Handoff pattern: The current agent transfers control to a specialist for a branch of work.
How to tell whether a product is acting as an agent
Compare observable behavior rather than relying on a product label. Useful questions include:
- Workflow control: Does the system 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 keep state across steps or sessions, and who manages that state?
- Approval and guardrails: Which actions need human approval, and what can happen without it?
- Stopping and handoff: Can it recognize completion, stop when it cannot proceed, or transfer control to a human or specialist?
- Measured performance: Does it perform well on the task you need, under realistic conditions?
Are AI agents better or safer than chatbots?
The label “agent” does not guarantee accuracy, reliability, or safety. The cited vendor documentation explains architectures and design practices; it does not establish a universal performance advantage for agents over chatbots. Assess the particular system on your own task, including how it handles errors, what actions it is allowed to take, and when a person reviews or approves those actions.
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Because agents may affect external systems rather than only produce text, their permissions and approval rules matter. A useful evaluation should examine the actual tools, limits, and handoff behavior—not just the product’s description.
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