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A chatbot mainly responds to a person’s prompt. An AI agent can use a model to direct a task through multiple steps, choosing tools, checking results, and adjusting what it does next. The distinction is about who or what controls the work—not whether the system has a chat window. A chatbot can also serve as the interface to an agent.
What separates an AI chatbot from an AI agent?
A chatbot is primarily a conversational interface. It can answer questions, draft text, summarize information, or help brainstorm. Those abilities alone do not make it an agent. OpenAI draws the line at whether a model controls workflow execution: a simple chatbot or single-turn application that does not do so is not an agent. OpenAI’s practical guide to building AI agents explains that distinction.
An agent is defined by its role in carrying out a task. It may decide which step to take, select an available tool, examine what happened, and continue—or ask a person for input. Anthropic contrasts workflows that follow predefined code paths with agents whose models dynamically direct their process and tool use. See Anthropic’s guide to building effective AI agents.
These are patterns, not mutually exclusive product categories. A system can present a chat interface while using an agent behind the scenes; a product calling itself an “agent” may still follow a largely fixed workflow. Look at how execution is controlled rather than relying on the label.
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How the three approaches compare
| Aspect | Chatbot or single-turn application | AI agent | Fixed workflow |
|---|---|---|---|
| Control of steps | Primarily responds to a user’s prompt. | The model may direct execution and choose tools as the task state changes. | Code defines the steps and paths in advance. |
| Typical fit | One-off answers, drafting, brainstorming, or open-ended exploration. | Multi-step goals that need decisions, tool use, or adjustment. | Stable, well-defined work where consistency matters. |
| Tool use | May have no tools or use one in a limited way. | Can select tools to gather context or take permitted actions. | Tools and actions are prescribed by the workflow. |
| Adaptation | Usually responds within the current interaction. | Can inspect results and adjust its process. | Changing the process generally requires changing its defined logic. |
| Oversight | The user interprets the response and decides what to do. | Guardrails and human check-ins can bound actions. | Prescribed paths make the process easier to anticipate. |
| Main tradeoff | Simple and direct for conversational tasks. | More flexible, but may add cost, latency, and complexity. | More predictable for clearly specified work. |
These are practical tendencies, not guarantees about every product. Systems can combine patterns, and vendors do not always use “agent” consistently.
What an agent does during a task
An agent may work in a loop: plan a step, act with an available tool, observe the result, and adjust its next step. It can repeat that cycle until it completes the task or needs a human check-in. Anthropic describes this plan–act–observe–adjust pattern in Trustworthy agents in practice.
For example, a chat assistant might explain how to prepare a report. An agentic system could retrieve information from approved sources, draft the report in a required format, check for missing fields, and then hand the draft to a person or take an authorized next step. This illustrates a possible design; it is not a claim that any particular product performs those actions.
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“Agent” does not mean unlimited or reliably independent. The system’s instructions, approved tools, and safeguards set boundaries. For consequential actions, it may need to pause, request approval, or hand control to a person.
How to tell whether a product is genuinely agentic
Ask questions about what the system actually does, not what its marketing calls it:
- Does it control a workflow, or only generate a response?
- Can it choose which step or tool to use next as the task develops?
- Does it inspect the result and adjust, or simply return a one-shot answer?
- Which actions is it allowed to take, and what safeguards restrict them?
- When does it stop and return control to a person?
A system that answers in chat but cannot direct task execution is functioning as a chatbot for that work. One that can choose steps, use tools, and respond to results is using agentic behavior, even if the user still interacts through chat.
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When should you use a chatbot, workflow, or agent?
Choose chat for one-off or open-ended work
Use a chatbot when you want an explanation, a draft, brainstorming, or exploratory help and you will decide what to do with the response. OpenAI Academy notes that regular chat is often a better fit for open-ended thinking and one-off tasks in its guide to workspace agents.
Choose a fixed workflow for repeatable work
Use a predefined workflow when the task is well specified and the same sequence should run consistently. Its steps are easier to anticipate, though changing the process means changing the defined logic.
Consider an agent when the task needs flexible decisions
An agent may suit multi-step work in which the next action depends on what the system finds or observes. The added flexibility is worthwhile only when it helps enough to justify potential cost, latency, and implementation complexity.
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Bound actions and decide where people step in
For an agent, define the permitted tools and actions, what requires approval, and when the system must stop or hand off. OpenAI’s guide describes guardrails and transferring control; Anthropic’s account highlights check-ins when human input is needed.
Why the interface is not the definition
Chat describes how a person communicates with a system; agency describes how the system carries out work. A chat window can front a system that only generates replies, or one that directs a multi-step process. Conversely, an agent does not have to look like a chatbot. To classify a product, examine who controls the steps, whether tools are chosen dynamically, and how results and oversight are handled.
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