Experiments should retire decisions, not fill a dashboard.

Video caption

Experiments should retire decisions, not fill a dashboard.

One wrong write can outweigh many fast routine cases.

Start with false-green and wrong-target controls.

My rule: Do this because scope is part of autonomy's value.

#EricFieldNotes

Full written post / accessibility read

Before funding an autonomous workflow, I would write the decision it must unlock. Can it handle a narrow queue without increasing exception hours or customer reversals? That question is more useful than a large collection of attractive demos.

A computer-use agent may save minutes on routine forms and create an expensive recovery when it changes the wrong account. The average runtime hides the tail. You need task slices by consequence, not one blended completion rate.

Test a resettable high-consequence fixture first: optimistic Save but no durable change, then same-name wrong account. Record whether the gate fails and whether a human can recover within the promised time. Only then spend effort polishing speed.

Approve only the task classes whose accepted outcomes and exception cost meet your threshold. Put the unresolved classes in an assisted lane with a named owner. Expand the envelope after new evidence, not after a more fluent sales pitch.

#EricFieldNotes

Four-beat scene transcript

1. Experiments should retire decisions, not fill a dashboard.

Before funding an autonomous workflow, I would write the decision it must unlock. Can it handle a narrow queue without increasing exception hours or customer reversals? That question is more useful than a large collection of attractive demos.

Visual: A leader needs the test that changes the investment call.

2. Failures have different economic meanings.

A computer-use agent may save minutes on routine forms and create an expensive recovery when it changes the wrong account. The average runtime hides the tail. You need task slices by consequence, not one blended completion rate.

Visual: One wrong write can outweigh many fast routine cases.

3. Rank tests by the decision they could reverse.

Test a resettable high-consequence fixture first: optimistic Save but no durable change, then same-name wrong account. Record whether the gate fails and whether a human can recover within the promised time. Only then spend effort polishing speed.

Visual: Start with false-green and wrong-target controls.

4. Fund the proven envelope.

Approve only the task classes whose accepted outcomes and exception cost meet your threshold. Put the unresolved classes in an assisted lane with a named owner. Expand the envelope after new evidence, not after a more fluent sales pitch.

Visual: Do this because scope is part of autonomy's value.

Research and claim limits

• OpenAI Computer use API guide (S159)
• OSWorld 2.1 official repository (S162)
• NIST AI RMF 1.0 (S171)

The examples identified as illustrative or simulated are design probes, not reported incidents. Vendor specifications do not establish workload performance.