More precise learned autonomy

Learned autonomy now recommends precise approval-policy changes based on the work people actually reviewed. Strong task-creation results can support direct task creation without also relaxing review for workflow updates, Skill changes, or unrelated work. We changed it because one strong track record could relax review for unrelated kinds of work too.

The same applies to Team routing: routine routes can become automatic while defer, block, or cancellation decisions remain under review.

Set clear approval boundaries

Agent and Team Autonomy settings now show the policy for each kind of work. Built-in autonomy levels provide a starting point, while Custom lets you choose Review or Direct for Agent work and Review or Automatic for Team routing.

Permissions remain separate. An Agent still needs permission to perform the work. Learned autonomy only determines whether that authorized work takes effect directly or waits for review.

Approved and rejected decisions contribute only to the matching kind of work. A task proposal cannot increase autonomy for workflow updates, and one Team routing decision cannot change the policy for another routing decision.

The Learned autonomy section shows the current policy, approval history, and confidence for each activity. Direct and automatic work is periodically returned to the normal review flow before taking effect, keeping the evidence current.

Keep every policy change human-approved

When the results support a change, Task Machine recommends moving that policy from Review to Direct, or back to Review, without changing other approval boundaries.

The recommendation arrives in the Inbox with the relevant decision history and human reasons. You can apply the change or keep the current policy. Learned autonomy never changes a policy without your approval.