AI agents concept

AI agents versus workflows

Reference

Agent vs chatbot: a chatbot mainly responds in conversation, while an AI agent can choose actions and continue through a task. A workflow is the sequence and control logic that organizes steps toward a goal; it may use fixed paths, an agent, or both. The practical difference is who chooses what happens next: the workflow’s predefined logic, or the agent responding to the task and results.

Reviewed 2026-10-09

Two ways to organize a task

A workflow is an arranged sequence of steps and rules for completing a task. In an AI workflow, a model or other tools may be called at specific points, but the surrounding code or process determines the path. For example, a process might classify a request, draft a reply, and send it for review in that order.

An AI agent is a system in which a model directs some of its own process and tool use toward a goal. It can choose a next step based on the task and what it observes, then continue, adjust, or ask a person for input. A workflow can include an agent, so these terms describe different parts of a system rather than mutually exclusive products.

The key difference is who directs the next step

In a predefined workflow, the path is laid out ahead of time: a trigger starts the process, rules choose branches, and each step passes its result along. This makes the process easier to inspect when the possible cases are known. A model may still interpret text or produce a draft inside one step without directing the whole process.

In an agent-directed loop, the model can select an action, receive the result, and use that result to decide what to do next. That flexibility can help with tasks whose steps depend on what is discovered along the way. It also means the result depends on the whole setup, including the model, available tools, instructions, permissions, and environment. An agent can operate inside a workflow that supplies its boundaries and stopping points.

When each is a good fit

Use a conventional workflow when a task is repeatable and its steps and exceptions can be written down clearly. Examples include routing a form by category or moving a record through a known approval sequence. If the task only needs a single response, a chatbot or one model call may be enough; adding an agent loop adds machinery without necessarily helping.

Consider an agent when the task is open-ended, involves ambiguous information, or needs different actions depending on what the tools return. Set boundaries around which tools it may use, when it should pause, and what counts as completion. Many useful systems combine both approaches: fixed workflow steps handle routine transitions, while an agent makes a bounded decision where the path is uncertain.

Common confusions

A chatbot is not automatically an agent. A chatbot that answers a question or drafts text is not directing a multi-step task merely because it uses AI. A chat interface can, however, be the way someone gives instructions to an agent, and a chatbot can call an agent behind the scenes.

Automation is broader than AI agents. Ordinary automation follows programmed rules or triggers; an agent can be used within an automated process when a model needs to choose among actions. Also, an agent does not mean a system acts without oversight: it can be configured to stop for approval or ask a person when it reaches a boundary. The useful question is not what a product calls itself, but which parts of the next step are predefined and which are chosen by the model.

Questions people ask

Can an AI agent be part of a workflow?

Yes. A workflow can set the start, boundaries, and handoffs, while an agent chooses some steps within it. The workflow and agent describe different roles in the system.

Does an AI agent always act without asking a person?

No. An agent can pause for approval or ask for information when it reaches a decision it should not make alone.

Is every chatbot an AI agent?

No. A chatbot that only responds to a message is not necessarily directing a task. It may serve as the interface to an agent that uses tools and decides subsequent steps.

Learn more

Building Effective AI Agents
Explains the architectural difference between predefined workflows and model-directed agents, with examples of when each fits.
A Practical Guide to Building Agents
Shows how tool use and model decisions distinguish an agent from a conventional automated workflow.

What changed

Sources

Part of AI agents: Agents, workflows, and harnesses Goals, action loops, and autonomy Reliability, permissions, and review