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What is the difference between an AI agent and a chatbot?
A chatbot is usually organized around conversation: you ask, it responds, and you decide what happens next. It can handle multiple turns, and some chatbots can use tools. The useful distinction is not whether the interface looks like chat, but whether the system controls a workflow and continues toward a goal.
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An agent uses a model to manage workflow execution. It can decide which step or tool is appropriate, observe the result, adjust its approach, recognize when a task is complete, and stop or return control when needed. OpenAI’s A practical guide to building agents distinguishes agents from applications that use an LLM without putting it in control of workflow execution. Anthropic describes the pattern as a self-directed loop: plan, act, observe, adjust, and repeat until the task is done or human input is needed, in Trustworthy agents in practice.
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What is each best suited to?
Choose a chatbot or direct model interaction for bounded work
- Getting an explanation or answer to a one-off question.
- Brainstorming, rewriting, drafting, or summarizing material for you to review.
- Handling work where the next step is a human decision or action, rather than a system continuing through a process.
A chatbot does not have to be limited to one reply. It is a good fit when the conversation itself is the useful result and you remain responsible for carrying out the next step.
Consider an agent for a multi-step workflow
- Gathering information from connected documents or services before producing a result.
- Repeating a process with stable inputs and a clear way to tell when it is complete.
- Taking permitted actions, such as updating a record, after checking relevant context.
OpenAI’s Agents documentation describes agents as systems that can plan, use tools, work with other agents, and retain context across steps. Those capabilities make a workflow possible; they do not establish that a particular agent will perform it reliably without testing and oversight.
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How do you decide which approach to use?
Start with the task, not the technology label. A direct model response is often the simpler starting point for bounded work. Agent behavior is worth considering when the value of delegating multiple steps and tool use outweighs the added integration, control, and monitoring effort.
| Decision factor | Question to ask | What points toward an agent? |
|---|---|---|
| Task shape | Is this a bounded exchange, or a goal that requires multiple steps and decisions? | A goal with a sequence of steps and decisions. |
| Repeatability | Does the process recur with stable inputs and a recognizable completion condition? | A repeatable process with a clear end state. |
| System access | Must the system retrieve information from or write to another service? | It needs specific connected tools, such as documents, a database, or a CRM. |
| Action authority | Should it retrieve or draft only, or may it change something? | Actions are narrowly defined and limited to what the task requires. |
| Human controls | Where must a person approve, take over, or resolve an uncertain case? | Approval gates, stopping rules, and escalation paths can be specified. |
| Operating tradeoffs | Can you accommodate complexity, latency, cost, and integration maintenance? | The value of automating the workflow justifies those operating demands. |
OpenAI’s agent guide identifies task complexity, latency, and cost as relevant model tradeoffs; it does not provide a neutral cross-vendor scorecard. Compare options against the requirements of your own workflow rather than assuming an agent is automatically faster, cheaper, or more accurate.
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How does an AI agent work?
OpenAI’s practical guide groups an agent’s core components into three parts:
- Model: reasons about the task and makes decisions.
- Tools: connect the system to external functions or APIs. Data tools retrieve context; action tools change or communicate with external systems; orchestration tools coordinate other agents.
- Instructions: define the agent’s behavior and guardrails, including what it may do and when it must stop.
In the OpenAI SDK, a run is not simply one model reply. The system calls the current agent, checks its output, executes any requested tool calls, and continues. A handoff can switch control to a specialist; the run ends when the agent returns a final answer without further tool work. The Running agents documentation describes this execution loop.
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When several agents are involved, the design determines who owns the user-facing answer. With a handoff, a specialist takes over that branch of the work. With agents used as tools, a manager calls bounded specialists and remains responsible for the final response. OpenAI explains these patterns in Orchestration and handoffs. Multiple agents are useful only when separating responsibilities or expertise helps; adding them by itself does not improve a workflow.
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What should you automate—and what should stay under human control?
An agent’s ability to act comes from the tools and permissions it has been given. Before relying on one, specify its access and authority, decide which actions need approval, and define how it should behave when it cannot complete the task or encounters an uncertain case.
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- Limit access: give the agent only the data and tools the task needs.
- Scope changes: distinguish reading or drafting from sending, updating, purchasing, or publishing.
- Set control points: require approval for consequential actions and define when the agent must stop or hand work to a person.
- Test and monitor: check representative cases and watch for errors or unexpected actions in operation.
OpenAI’s guide explicitly includes guardrails, completion recognition, and the ability to halt or transfer control. Treat “agent” as a description of system design, not a guarantee of correctness or dependable autonomy. What it can safely do depends on the particular workflow, tools, permissions, and controls.
When can a repeatable workflow benefit from an agent?
A useful way to frame an agent-driven workflow is as a trigger, a process, and approved tools or systems. A trigger might be a schedule or a manual run; the process describes the steps to complete; approved tools retrieve information or take permitted actions. OpenAI Academy’s Workspace agents guide, published April 22, 2026, uses this framing for repeatable work. It helps identify where repeated manual steps might be delegated, but it is not a reason to automate every process.
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