I like computer-use agents for a practical reason: I can give one a goal instead of a script. It can inspect an unfamiliar screen, decide what to do next, and recover from situations I didn’t predict.

That flexibility has a cost. The model has to inspect and decide again on every run, even when the task has become routine.

The useful OpenAdapt vs. computer-use-agent question is about the shape of the work: should each run interpret the task again, or should it replay a reviewed program and stop when reality differs?

Start with the amount of novelty

A computer-use agent is the better fit when the task is exploratory. Perhaps you need to find a setting in unfamiliar software, triage a changing queue, or complete a job that may follow a different path each time. A plain-language goal is enough to start. You don’t need to demonstrate and qualify one exact workflow first.

OpenAdapt fits a repeated GUI transaction. A person demonstrates the task, and the compiler turns that evidence into a reviewable program. Healthy runs execute the compiled steps deterministically. Models can help during compilation or a governed repair, but the healthy run doesn’t ask one to plan the task again.

This distinction matters more than the vendor name. A novel task benefits from fresh reasoning. A repeated consequential transaction benefits from a stable program, declared checks, and a clear halt.

Look at the 500th run, not the first

The first successful agent run is persuasive because it starts with almost nothing. Repetition changes the calculation.

OpenAdapt’s retained MockMed benchmark compared both approaches on one short task. The compiled arm replayed one recorded workflow 100 times. The computer-use arm started from the goal and current screenshot 20 times. Both completed every retained run.

The observed difference was time and model use. The compiled arm had a 4.9-second median and made zero model calls. The agent arm had a 37.5-second median and used the model on every run, with a reported list-price model cost of $0.2716 per run.

Those figures were measured on 2026-07-08 with a pre-v0.2.0 source checkout that declared Flow 0.1.0. The exact runtime commit wasn’t retained, and the result hasn’t been re-measured on a later release. The agent sample was smaller. This was one synthetic application and one task, so it doesn’t establish a general reliability difference.

It does show the operating difference cleanly. The agent reasons again. The compiled workflow reuses reviewed work. The 500th-run report includes the setup and caveats, with links to the raw results and cost basis.

Decide who can prove the result

A screenshot can show a success banner while the system of record rejects the write. The same screen can also display the wrong customer, an old value, or a duplicate created after an uncertain retry.

OpenAdapt separates the action from the result check. A browser or desktop session can click Save. A configured verifier then reads an independent source such as a supported API, a database view, or an exact file. The run returns VERIFIED only when that evidence proves the full effect contract.

Uncertain delivery gets a different outcome. If the action may have reached the application and the independent evidence remains inconclusive, OpenAdapt returns RECONCILIATION_REQUIRED. It keeps the evidence and suppresses a blind replay.

Computer-use agents can inspect the screen after acting, and their host application can add approval gates or external checks. Providers also recommend human oversight for consequential actions. The important question is whether your implementation has a separate source that can grade the business effect. The acting session shouldn’t be its only judge.

Treat drift as a product decision

Computer-use agents earn their place when the interface keeps surprising you. They can reason from a fresh screenshot and choose a new path without waiting for someone to author that path first.

OpenAdapt takes a stricter route. It replays the qualified program while the live evidence still supports it. When the interface drifts, it can re-resolve from retained evidence or propose a reviewable repair. A failed identity check, ambiguous target, or refuted effect stops the run.

That behavior is valuable when an imaginative recovery would be dangerous. It can be too restrictive when exploration is the job. If you expect the task to change every week, requiring a qualified workflow version for each material change may add work without enough benefit.

Use both where their boundaries meet

The two approaches can occupy different stages of the same process. A computer-use agent can explore an unfamiliar application or help an operator find the path. Once the transaction becomes stable and frequent, an operator can demonstrate the bounded task through OpenAdapt. The compiler can then produce a program for review and qualification.

This is an architecture pattern, not a published connector between OpenAdapt and a particular agent provider. A real deployment still needs the correct identity contract, authorization, and effect verifier inside its data boundary.

My decision rule is short:

  • Use a computer-use agent when the work is novel enough to justify fresh reasoning on each run.
  • Use OpenAdapt when the same consequential task repeats and each run should follow a reviewed program.
  • Keep a person in control of high-impact actions until the exact workflow has the evidence and policy it needs.

Try the transaction contract locally

The OpenAdapt quickstart runs a small synthetic workflow and verifies the saved record through a separate read-only interface.

python -m pip install --upgrade openadapt

openadapt quickstart

The first-workflow guide moves from that fixture to your own web application. The OpenAdapt vs. computer-use agents comparison covers drift, per-run model use, effect verification, data locality, and supported surfaces with the current source links.