Oliver Kieselbach 8da8dc649d v2 codex-06: ReducerVerifier semantic replay — reducer-agreement CI gate
Closes Codex review follow-up #6. Until now
GetSessionReducerVerificationFunction only ran structural checks
(ordinal contiguity, step-index contiguity, ReducerVersion drift,
orphaned SignalOrdinalRef). That catches corruption but never verifies
the backend's reducer agrees with the agent-produced journal under the
same ReducerVersion — exactly the invariant the whole
(newState, transition, effects) architecture rests on.

Unblocked by Codex #1 phase 1: ReducerReplay.Replay exists in
DecisionCore, and the backend already references DecisionCore.

Flow:
  1. Skip preconditions: drift, ordinal gaps, step-index gaps, empty
     session, or head PayloadJson deserialization failure → record
     SemanticReplaySkipReason + optionally emit an Info 'replay_skipped'
     issue. Never throws.
  2. Deserialise each SignalRecord.PayloadJson via SignalSerializer.
  3. Fold through a transient DecisionEngine: state = Reduce(state, sig).
  4. Compare each produced transition against the stored one on
     (StepIndex, Trigger, FromStage, ToStage, Taken, DeadEndReason).
     Discrepancies surface as 'replay_divergence' issues (capped at
     20 individual entries; overflow message follows the same pattern
     as the structural gap reports).
  5. Compare replayed final state's Stage to the last stored
     transition's ToStage — mismatch surfaces as 'replay_final_stage_mismatch'
     at Error severity.

Report additions (backward-compatible):
  - SemanticReplayPerformed
  - SemanticReplaySkipReason
  - SemanticReplayFinalStageMatches
  - ReplayedFinalStage
  - TransitionDivergenceCount

Side change: GetSessionReducerVerificationFunction XML-doc no longer
claims "structural only" — now documents both layers and the skip
rules.

Tests (+7, ReducerVerifierSemanticReplayTests):
  - Skip on ReducerVersion drift.
  - Skip on signal-ordinal gap.
  - Skip on step-index gap.
  - Perfect agreement (reducer-seeded journal round-trips).
  - Divergence: tampered Trigger → one replay_divergence issue.
  - Divergence: tampered last-ToStage → replay_final_stage_mismatch Error.
  - Mid-stream deserialization failure tolerated without aborting.

Existing structural ReducerVerifierTests unchanged — their stub
PayloadJson='{}' deserialization failures now silently set
SemanticReplaySkipReason='deserialization_failure' without polluting
Issues, preserving the happy-path empty-issues assertion.

Suites green: DecisionCore 151/151, Agent.V2.Core 643/643,
Functions 734/734 (+7). Build 0 warnings.

Plan: .claude/plans/codex-06-reducer-verifier-semantic-replay.md

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-04-24 11:32:56 +02:00
2026-04-22 19:36:19 +02:00
2026-03-17 18:24:01 +01:00
2026-03-26 07:02:33 +01:00
2026-04-04 19:17:01 +02:00

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.

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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
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Architecture

For detailed information about the system architecture, components, and data flow, see Architecture Documentation.

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See LICENSE for full details.

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Windows Autopilot & Intune enrollment monitoring: live session timelines, fleet health, diagnostics, and an MCP server for AI-assisted troubleshooting.
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