Oliver Kieselbach 82919d4791 session lifecycle: reliable end-detection for all deployment scenarios
Multi-signal enrollment completion with 3 independent paths:
- IME pattern (existing), ESP final exit + Hello (composite),
  Desktop arrival + Hello (no-ESP/WDP v2)

ESP failure detection: terminal failures emit enrollment_failed immediately,
recoverable failures (ESPProgress_Timeout) get 1-min grace period with
recovery cancellation on ESP phase change.

Agent max lifetime timer (6h default, configurable per-tenant) as safety
net against zombie agents. Desktop arrival detector polls explorer.exe
with WMI owner validation. Enrollment state persistence for crash recovery.

Completion check events with full state machine snapshot for observability.
Backend maintenance interval reduced from daily to every 2 hours.
ConfigVersion 12 → 13.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-03-03 23:12:26 +01:00
2026-02-10 02:37:31 +01:00
2026-02-23 22:06:03 +01:00
2026-02-05 06:14:41 +01:00
2026-02-05 06:14:41 +01:00
2026-02-24 11:50:31 +01:00

Autopilot Monitor

Advanced monitoring and troubleshooting solution for Windows Autopilot deployments.

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

MIT License — see LICENSE for details

S
Description
Windows Autopilot & Intune enrollment monitoring: live session timelines, fleet health, diagnostics, and an MCP server for AI-assisted troubleshooting.
Readme AGPL-3.0 60 MiB
Languages
C# 72.7%
TypeScript 25.7%
PowerShell 1%
JavaScript 0.5%