Apprenticeship substrate

@@ -19,9 +19,9 @@ paired_with: apprenticeship-substrate.es.md

When a senior engineer reviews a junior's work, the correction is the most valuable artifact in the room — and almost every organization discards it. The reasoning behind a fix is spoken once, applied once, and never captured. The next junior, and the next model, start from zero.

The apprenticeship substrate captures it. <!--claim id=route-and-capture confidence=structural cites=[]-->Every code-shaped and editorial task is routed first through a local [[service-slm|Small Language Model]]; a senior reviewer then signs a verdict on the attempt. The signed disagreement between apprentice attempt and senior verdict becomes preference-pair training data.<!--/claim-->
The apprenticeship substrate captures it. Every code-shaped and editorial task is routed first through a local [[service-slm|Small Language Model]]; a senior reviewer then signs a verdict on the attempt. The signed disagreement between apprentice attempt and senior verdict becomes preference-pair training data.

<!--claim id=three-stage-routing confidence=structural cites=[]-->Routing runs in three stages per task type — `review`, `spot-check`, and `autonomous` — with quantified promotion thresholds. A graduated task type eliminates senior-author tokens on that class of work, and shadow routing harvests training data from every other code-shaped commit across the fleet.<!--/claim-->
Routing runs in three stages per task type — `review`, `spot-check`, and `autonomous` — with quantified promotion thresholds. A graduated task type eliminates senior-author tokens on that class of work, and shadow routing harvests training data from every other code-shaped commit across the fleet.

For a regulated buyer the consequence is concrete. The AI improves on the organization's own work, and that work never leaves the deployment. Per `[ni-51-102]` [[disclosure-substrate|continuous-disclosure]] language, the trajectory toward token elimination across graduated task types is forward-looking; the routing structure is operational today.

@@ -45,7 +45,7 @@ Routing operates per task type. Promotion is automatic on a threshold crossing; 
| `spot-check` | Apprentice commits; senior reviews a 1-in-N sample and auto-flagged anomalies | Sampled and flagged |
| `autonomous` | Apprentice commits autonomously; monthly batch audit | Batch audit |

<!--claim id=promotion-thresholds confidence=structural cites=[]-->Promotion from `review` to `spot-check` requires at least 50 verdicts at a 0.85 acceptance rate over the rolling 50. Promotion to `autonomous` requires at least 100 verdicts at a 0.95 acceptance rate over the rolling 100, with zero post-commit reverts traced to apprentice diffs.<!--/claim--> A single revert traced to an apprentice diff drops the task type one stage, recorded as a signed ledger event. New task types start at `review`.
Promotion from `review` to `spot-check` requires at least 50 verdicts at a 0.85 acceptance rate over the rolling 50. Promotion to `autonomous` requires at least 100 verdicts at a 0.95 acceptance rate over the rolling 100, with zero post-commit reverts traced to apprentice diffs. A single revert traced to an apprentice diff drops the task type one stage, recorded as a signed ledger event. New task types start at `review`.

## The brief, the attempt, the verdict

@@ -53,7 +53,7 @@ A senior who would author a diff issues a **brief** instead. The brief states wh

The apprentice responds with an **attempt**: reasoning that cites the brief's invariants, a self-confidence value calibrated against its prior ledger record on this task type, and a unified diff. If self-confidence falls below 0.5, the apprentice escalates without a diff — surfacing that the task type is harder than it can handle today, rather than wasting senior review on a low-confidence diff.

The senior reads the attempt and signs a **verdict**: `accept`, `refine`, `reject`, or `defer-tier-c`. <!--claim id=verdict-signature confidence=structural cites=[]-->Verdicts of `refine` and `reject` carry a one-sentence note — the highest-signal training data the corpus produces. The signature uses `ssh-keygen -Y sign` with a namespace tag that binds it to this protocol, so a commit-signing signature cannot be repurposed as a verdict.<!--/claim-->
The senior reads the attempt and signs a **verdict**: `accept`, `refine`, `reject`, or `defer-tier-c`. Verdicts of `refine` and `reject` carry a one-sentence note — the highest-signal training data the corpus produces. The signature uses `ssh-keygen -Y sign` with a namespace tag that binds it to this protocol, so a commit-signing signature cannot be repurposed as a verdict.

## The promotion ledger

@@ -65,9 +65,9 @@ The event schema is closed — `task-type-add`, `verdict-batch`, `promotion`, `d

Two paths run in parallel.

<!--claim id=production-routing confidence=structural cites=[]-->Production routing runs on graduated task types: the senior issues a brief before authoring a diff, the apprentice's attempt is the candidate diff, and on `accept` that diff lands in the commit. This eliminates senior authoring tokens on graduated task types.<!--/claim-->
Production routing runs on graduated task types: the senior issues a brief before authoring a diff, the apprentice's attempt is the candidate diff, and on `accept` that diff lands in the commit. This eliminates senior authoring tokens on graduated task types.

<!--claim id=shadow-routing confidence=structural cites=[]-->Shadow routing runs on every other code-shaped commit across every active cluster. After the diff is authored the usual way, the session fires a brief to the apprentice; the (brief, attempt, actual-diff) triple is captured to the corpus with no verdict and no signing.<!--/claim-->
Shadow routing runs on every other code-shaped commit across every active cluster. After the diff is authored the usual way, the session fires a brief to the apprentice; the (brief, attempt, actual-diff) triple is captured to the corpus with no verdict and no signing.

Production routing eliminates senior tokens on graduated types. Shadow routing generates the training data that graduates the next type. The two paths compound.

@@ -79,13 +79,13 @@ The apprenticeship corpus is a fourth corpus alongside the constitutional, engin
data/training-corpus/apprenticeship/<task-type>/<tenant>/<ulid>.jsonl
```

One file holds one (brief, attempt, verdict) triple. <!--claim id=tenant-private-corpus confidence=structural cites=[]-->Tenant-private records never leave the tenant's infrastructure.<!--/claim--> A `refine` or `reject` verdict additionally produces a Direct Preference Optimisation triple — rejected attempt, corrected diff, constraint-violation tag — which feeds adapter training on the apprentice's policy.
One file holds one (brief, attempt, verdict) triple. Tenant-private records never leave the tenant's infrastructure. A `refine` or `reject` verdict additionally produces a Direct Preference Optimisation triple — rejected attempt, corrected diff, constraint-violation tag — which feeds adapter training on the apprentice's policy.

## Configuration

The first registered task type is `version-bump-manifest`. Every platform MINOR and PATCH bump touches `MANIFEST.md` and `CHANGELOG.md`: well-shaped work, no architectural judgment, easily verifiable. The apprentice graduates this type first; senior tokens drop on this class of work; the next task type registers.

<!--claim id=four-conditions confidence=structural cites=[]-->Four conditions make the substrate work, and all four are structural properties of a [[customer-hostability|customer-owned deployment]]: a per-customer governance charter, per-customer signing identities, per-customer task-type granularity in the promotion ledger, and per-customer continued pretraining.<!--/claim--> A cloud-managed AI platform lacks all four — training on customer interaction data requires pooling it, which eliminates the per-customer isolation guarantee.
Four conditions make the substrate work, and all four are structural properties of a [[customer-hostability|customer-owned deployment]]: a per-customer governance charter, per-customer signing identities, per-customer task-type granularity in the promotion ledger, and per-customer continued pretraining. A cloud-managed AI platform lacks all four — training on customer interaction data requires pooling it, which eliminates the per-customer isolation guarantee.

The end state is a continuum: code-shaped work the apprentice handles autonomously, work it handles under spot-check, and work that still requires senior review. The continuum shifts as the corpus matures. Per `[ni-51-102]`, the trajectory toward token elimination is forward-looking; the [[adapter-composition|routing shape]] is in place today.