Oliver Kieselbach e19852cd9a add structured error code extraction from IME log patterns
The backend already reads errorCode from event data for FailureCode and
failure reason formatting, but the agent never populated it. This change
extracts error codes (exit codes, HRESULT) from named regex capture groups
in IME log error patterns and sends them as structured errorCode fields.

Also fixes a pre-existing bug where ErrorPatternId/ErrorDetail were not
persisted across agent restarts.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-04-03 16:56:00 +02:00
2026-03-31 23:30:43 +02:00
2026-04-02 01:00:47 +02:00
2026-04-01 00:27:23 +02:00
2026-03-31 03:41:22 +02:00
2026-04-01 04:33:45 +02:00
2026-03-31 22:29:24 +02:00
2026-03-17 18:24:01 +01:00
2026-03-26 07:02:33 +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

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.

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%