The two categories overlap
Browser automation describes software controlling a browser. Computer use describes a decision-and-action loop over an interface. An AI browser agent can be both. The useful buying question is whether your workflow needs adaptive decisions, explicit scripted steps, or both. Comparing the labels alone hides this design choice.
Playwright documents locators that identify page elements and support browser interactions. A script can use these controls to execute a known flow. An agent can also use a browser controller underneath its planning loop. The presence of a language model does not remove the need to locate elements, wait for state changes, or verify the destination.
Use explicit flows for a known contract
Consider a daily export from an application you own. If the export path and completion conditions are stable, a deterministic test or job is a strong baseline. You can define exactly which report is selected, which failure stops the job, and which file indicates completion. A model should earn a role by solving a specific ambiguity the existing flow cannot handle.
Now consider a permitted research task across unfamiliar pages. The next useful link may depend on page meaning rather than a known selector. Adaptive navigation becomes a candidate, but extraction should still return a schema that your code validates. The flexible portion and the strict portion need separate acceptance rules.
Design a hybrid at the handoff
An editorial architecture is to let a constrained agent find the relevant record, then pass a stable identifier to deterministic code for the final operation. Another is to run the normal script first and send an unfamiliar layout to review. Avoid an unrestricted fallback that converts every script failure into permission for an agent to improvise.
The handoff should include the current account, target record, observed URL, allowed operation, and expected result. Check that these still match immediately before execution. A browser tab may navigate or refresh while an agent is deciding, so the most recent observation matters more than a remembered screenshot.
Evaluate the boundary, not a demo
Measure accepted task completion under the same starting conditions. Count retries and human rescue, and test whether the system stops when the target is ambiguous. For an adaptive flow, inspect wrong-but-confident actions. For a deterministic flow, inspect brittle assumptions. Neither category provides a universal reliability guarantee.
Our recommendation is to begin with the smallest controller that can complete the task and add model decisions where uncertainty requires them. Browser-only evidence should not be treated as proof of native desktop support. Use environment-specific profiles and workflow constraints to make that distinction visible in procurement.