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Oliver Kieselbach
f75a8d4375
Plan §4 M3.3: the first kernel-service classifier goes live (plan §2.4 L.8),
the effect-based classifier-run contract is wired end-to-end, and the anti-loop
via InputHash is proven by a dedicated fixture.
Classifier (WhiteGloveSealingClassifier : IClassifier):
- Scoring ported 1:1 from Legacy WhiteGloveClassifier (same weights, same
thresholds, same asymmetric-conservative rule: only Confirmed drives the
WhiteGloveSealed stage).
- Positive weights: ShellCoreWhiteGloveSuccess=80, SealingPattern=40,
DeviceOnlyDeployment=15, SystemReboot=15.
- Hard excluders (-100 each): AadJoinedWithUser, DesktopArrived, HelloResolved.
Soft exclude: AccountSetupActivity=-40.
- WhiteGloveSealingSnapshot is the classifier input type and computes a
deterministic SHA256-short InputHash over WG-relevant fields only — so
the anti-loop triggers on meaningful fact changes, not on unrelated state.
Handlers (DecisionEngine.WhiteGlove.cs):
- WhiteGloveShellCoreSuccess records fact; emits RunClassifier effect;
arms ClassifierTick if not already.
- WhiteGloveSealingPatternDetected mirror handler for the IME-pattern path.
- ClassifierVerdictIssued applies the verdict to WhiteGloveSealing
hypothesis; on Confirmed transitions to
WhiteGloveSealed stage +
Outcome=WhiteGlovePart1Sealed and emits
the whiteglove_complete event.
- HandleClassifierTickDeadlineFired (shared router) re-emits RunClassifier
and re-arms itself unless terminal.
ClassifierAwareReplayHarness (tests-only):
- Loops per-step effects: any RunClassifier effect is resolved synchronously
via the registered IClassifier; the verdict is wrapped in a
ClassifierVerdictIssued signal and fed back into Reduce. Anti-loop skips
the call when snapshot hash == state.WhiteGloveSealing.LastClassifierVerdictId.
- Exposes ClassifierRunStats for regression tests.
DecisionState extension: two new SignalFact<bool> fields capture the WG
sightings (ShellCoreWhiteGloveSuccessSeen, WhiteGloveSealingPatternSeen). The
classifier reads them via WhiteGloveSealingSnapshot.
Scenario fixtures (4, committed-anonymized):
- whiteglove-inline-v1.jsonl Confirmed on ShellCore alone (80).
- whiteglove-signal-correlated-v1.jsonl Weak after pattern, ClassifierTick
anti-loop-skipped, Confirmed after
ShellCore escalates the score.
- whiteglove-false-positive-v1.jsonl SealingPattern + late-AADJ hard-
excludes -> Rejected; session still
completes via Classic Hello+Desktop.
- whiteglove-antiloop-v1.jsonl Single pattern + two ticks; runs=1,
skipped_by_antiloop=2.
Tests: 4 scenario tests in WhiteGlovePart1ScenarioTests, all passing.
DecisionCore.Tests: 49 (was 45). Legacy 507 + Functions 557 unchanged.
ReducerFailSafeTests + HarnessTests updated to use AppInstallCompleted as
the still-unhandled sentinel (WhiteGloveShellCoreSuccess is now wired).
Plan: plans/REFACTOR_AGENT_V2.md §4 M3 sub-milestone M3.3 + §2.4 classifier kernel.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Autopilot Monitor
Advanced monitoring and troubleshooting solution for Windows Autopilot deployments. Gain full visibility into every enrollment session with a detailed event timeline, fleet health dashboards, and session reporting. Define custom analysis rules to automatically detect issues and gather rules to collect targeted evidence. Retrieve diagnostics packages on demand, configure agent settings like auto-reboot behavior and automatic timezone adjustment — all managed centrally from the web dashboard.
Private Preview
Autopilot Monitor is currently running as a Private Preview. Visit autopilotmonitor.com to request access and learn more.
Overview
Autopilot Monitor provides real-time tracking, intelligent diagnostics, and automated troubleshooting for Windows Autopilot enrollment processes. It consists of:
- Bootstrap Script — PowerShell script deployed via Intune that starts monitoring early in the enrollment process
- Monitoring Agent — Lightweight .NET application that collects telemetry and evidence during enrollment
- Backend API — Azure Functions-based ingestion and processing pipeline
- Web Dashboard — Next.js application for real-time monitoring and fleet analytics
Architecture
For detailed information about the system architecture, components, and data flow, see Architecture Documentation.
Documentation
Full documentation is available at autopilotmonitor.com/docs
License
This project uses a split licensing model:
- MIT License — Agent (
src/Agent/) and Shared library (src/Shared/) — unrestricted use on end-user devices - AGPL-3.0 — Backend (
src/Backend/), Web Dashboard (src/Web/), and MCP Server (src/McpServer/) — server-side components remain open source
See LICENSE for full details.