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Clear out junk files and repair common Windows errorsFree Scan →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Repair Windows errors before they cause bigger problemsFix Now →A workflow follows steps chosen in advance; an AI agent can decide what to do next as a task unfolds. The useful dividing line is not whether a process uses AI, but who directs its execution. If a fixed process includes an AI step to classify or summarize something, it is still a workflow. If the system chooses tools, adapts its plan to new information, and decides when to stop or ask for help, it is acting as an agent.
What separates a workflow from an agent?
A workflow is an ordered sequence of steps designed to reach a goal. OpenAI describes it as “a sequence of steps that must be executed to meet the user’s goal,” such as resolving a customer-service issue or generating a report. In a conventional automated workflow, the steps and rules are specified ahead of time.
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An agent uses a model to manage more of the execution: it can choose among available tools or actions, respond to what it learns, and decide whether to continue, change approach, or hand control back. The distinction is about decision-making during execution, not simply whether the system contains a language model.
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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11| Question | Workflow | Agent |
|---|---|---|
| Who chooses the next step? | The process follows predefined steps and branches. | The system can select a next tool or action as the task proceeds. |
| How does it handle changing conditions? | It follows the branches already specified; unexpected cases may require a fallback or human handoff. | It can adjust its approach in response to new information, within its allowed scope. |
| Where does interpretation happen? | An LLM may handle a bounded step, then return control to the workflow. | The model manages a larger share of task execution and tool choice. |
For more detail on the terminology, see OpenAI’s practical guide to building agents and its guide for business leaders.
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When should you choose a workflow?
Choose a fixed workflow when the task is repetitive, its steps are stable, and predictable execution or auditability matters more than flexibility. For example, a process that checks submitted fields, routes a request based on known rules, and records the outcome can be easier to inspect when its path is defined in advance.
- Use fixed rules for steps that are consistent and can be described clearly.
- Plan for exceptions with explicit branches, error handling, or a handoff when an input does not fit.
- Keep the process inspectable so a reviewer can see which steps ran and where a failure occurred.
The trade-off is rigidity: a predefined process may not respond gracefully when circumstances fall outside its branches. That does not make a workflow a poor choice; it makes the boundaries and exception path important design decisions.
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When does an LLM step still belong to a workflow?
A process does not become an agent merely because it uses an LLM. If the overall sequence remains fixed and the model performs one bounded task—such as classifying a message, summarizing a document, or extracting fields—the system is still a workflow with an AI-powered step.
One practical design is to have the model return a classification or structured result, then let the workflow apply established rules to that result. This keeps interpretation separate from decisions about what actions the system is authorized to take.
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When is an agent a better fit?
Consider an agent when a task begins with a goal but not a complete recipe. The system may need to choose among tools, react to results, revise its approach, or ask a person to clarify an ambiguity. This adaptability can help when the next step depends on information that is not known in advance.
Before giving an agent more authority, define the boundaries of its work:
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- Which tools and actions may it use?
- What information may it access, and what must remain out of reach?
- Which conditions require it to stop, ask for clarification, or transfer the task to a person?
- How will a failed action be detected, recorded, and recovered?
The more the system can change its own execution path, the more important it is to design guardrails, failure handling, and handoffs deliberately.
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Consider the consequence of a wrong action, not just whether a task can be automated. A draft that someone will review before use calls for a different level of control from an action that affects a customer or changes a business record. Higher-consequence actions warrant deliberate validation and approval arrangements.
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Automation does not transfer responsibility for how its output is used. Microsoft’s guidance on choosing Copilot or an agent says people who automate a task or part of a workflow remain responsible for reviewing, validating, and approving the work. An approval step is most useful when the reviewer has enough context to assess the proposed action.
A practical decision rule
- Write down the goal and the steps. If you can specify a stable sequence and its branches in advance, start with a workflow.
- Mark the points that need interpretation. If only one or a few steps require judgment, consider using an LLM for those bounded steps while leaving the rest of the process fixed.
- Check whether the next action depends on new information. If the system must choose tools or revise its plan as results arrive, an agent may be appropriate.
- Set authority and handoffs before deployment. Specify permitted actions, review points, failure behavior, and when a person must take over—especially for consequential actions.
These patterns can be combined. A workflow can govern the overall process while an agent handles a bounded task inside it. Rather than relying on the label “agent,” describe which parts are fixed, where judgment occurs, and what the system can do without human approval.
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