AI agents vs. workflows: what is the difference, and which should you use?
Compare AI agents and predefined workflows by who decides the next step, suitable tasks, operating costs and maintenance. Learn how to combine them and evaluate both on the same tasks.
Choose an approach
Workflows mainly organize steps using predefined rules, while agents let a model choose subsequent actions based on a goal and tool results. For tasks with clearly defined steps, a workflow is usually easier to debug first. When each finding changes what to do next, an agent may help within a bounded scope. They can be combined: a fixed workflow can validate inputs and deliver results while an agent handles the investigation. This explanation uses Anthropic’s architectural distinction; a product’s “agent” label alone does not reveal how it operates.
The distinction is who chooses the next step
An AI workflow can call models, branch and retry. If code or configured rules mainly determine the next step, it is useful to treat it as a workflow. For example, extracting an order ID, querying an order system and drafting a response from the result does not require the model to redesign the whole process.
An agent may choose another action after reading a tool result, such as checking the carrier’s information when tracking records are insufficient. The application determines which tools are available; the model does not gain account or system permissions from a label. One model call is not necessarily an agent—the relevant question is whether it directs subsequent execution.
Compare suitability using the same tasks
For a hypothetical “My order is late; find out why” task, give both approaches the same order data and read-only tools. Define which missing details require clarification and which results support an explanation. The table is an evaluation design; we did not run a customer-service system or benchmark a framework.
| Comparison condition | Predefined workflow | AI Agent |
|---|---|---|
| Matched inputs | The same order questions, data and tools | The same inputs and query permissions |
| Suitable conditions | Known steps with branches that can be specified | Required steps change with findings |
| Cost and maintenance | Maintain rules and model calls at each step | Maintain tools and stopping rules; monitor repeated calls |
| How to evaluate | Check branches, failure handling and the final response | Also check tool selection and argument accuracy |
Combine them around the variable subtask
A workflow can validate an order ID, pass cases requiring investigation to an agent, and format the result consistently afterward. An agent can also call a packaged workflow for repeated steps. The OpenAI Agents SDK documents both code-driven and model-driven orchestration; the choice should follow the task rather than the product name.
Adding more agents before one agent chooses tools reliably introduces handoff and state-coordination problems. Start with a bounded investigation and limits on repeated calls. Preserve tool failures and return them to a defined handling step. This makes it possible to check whether the extra flexibility solves tasks the simpler approach could not.
Evaluate both results and tool actions
Repeat the same question, record actual tool calls and results, and check the basis of the final response. For workflows, cover the different branches. For agents, also inspect incorrect tool choices and arguments. A model’s claim that it found information cannot replace a record returned by the system.
Record latency and usage for each task, including missing-data and tool-error cases. A universal call-count limit is not appropriate for every project; set criteria around acceptable waiting time and consequences of errors. We reviewed the official sources on October 20, 2026 and did not measure the success rate of any implementation.
Checked sources
These sources support the concepts and methods described here. Examples and test scope are labeled separately; check the version of the tools you use.