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In plain terms: L7 watches what happens after a decision. Did the build go as predicted? Did the clearance match the as-built measurement? Those real outcomes become signals that improve the system over time.

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What it does

L7 closes the loop from approved decisions to later outcomes. Approved decisions get tracked against actual results:
  • Was the rework predicted?
  • Did the material spec hold?
  • Did the build-readiness assessment match production reality?
Supersession events, rework events, and partner feedback all become training signals for future capability improvements.

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The learning flywheel, with a brake

L7 is the learning flywheel, but it has a deliberate brake on it:
The flywheel only turns once governance is stable, and it never turns on tenant-private data without explicit permission.
In other words, the system learns from outcomes, but a yard’s private data is never used to train anything without that yard’s explicit consent. See Keeping Your Data Yours.

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What L7 outputs

  • outcome_event
  • eval_result
  • capability_update_proposal
  • training_example

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How it connects to capability promotion

A capability does not get more autonomy just because someone wants it to. It earns promotion through observed performance. A capability that misses its quality targets stays in observe-only or draft-only mode; it is not promoted to propose-with-review or execute-after-approval until L7’s evidence shows it has earned it. See How We Measure Quality for the targets that gate this.