Oliver Kieselbach
197de1d718
IME's trace listener opens every log once per process with FileMode.Append and FileShare.ReadWrite and writes at its own stream position afterwards. AgentExecutor.exe (two processes per user-context script, one per detection script) has such a listener on AgentExecutor.log and on IntuneManagementExtension.log, so a process that resumes logging after another one appended overwrites those bytes: the final file holds the last writer, a byte-position tailer has read the first version. Session 46749560 lost 6 of 16 platform-script results and 4 of 16 executor end blocks with every tracker health counter at zero — results fell back to the exit code (one Success where IME said Failed), one finished script stayed Incomplete. The agentexecutor_fallback results in the fleet are this race, not late IME logging. The tracker now keeps a fingerprint ledger of every entry it processed from the two multi-writer files (FNV-1a 64 over the decoded bytes, one CmTraceEntryAssembler shared by the read loop and the verification so entry boundaries are identical by construction), verifies it on every script end signal, on a byte-budgeted cadence while an invocation is in flight (256 KB/s, at least 1 s apart), in a 4 KB guard zone on every growth pass and on a fragment at the bookmark, and rewinds to the first changed entry; entries whose (offset, hash) still read unchanged are skipped, everything else is processed as backlog (never fresh, never a calibration anchor). The policyId-less executor lines are owned by the nearest preceding invocation marker in their file (PS-AGENT-ARG opens, PS-AGENT-SCRIPT-START upgrades to the platform script, PS-AGENT-INVOCATION and PS-AGENT-COMPLETED close) instead of a last-started pointer, so an end block a rewind uncovers reaches its own script. Restart seeds the ledger from the 64 KB behind the persisted bookmark; a rollover clears it. Four restart-safe counters (ime_overwrite_rewinds, ime_overwrite_bytes_reprocessed, ime_verify_passes, ime_verified_bytes) ride agent_metrics_snapshot and ime_pattern_hits — recovered data, not skipped work, no new event type. Tests: ImeLogTrackerOverwriteRewindTests (15) drive the positional-overwrite write pattern against both files, including the two field scenarios with the shipped patterns; agent suite 2921 green, backend known-actions list extended for the two new patterns. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
Autopilot Monitor
Autopilot Monitor is an open-source, real-time monitoring and troubleshooting platform for Windows Autopilot and Microsoft Intune enrollment. It records enrollment events, application installations, policies, device state, and diagnostics while provisioning is still running, instead of collecting logs by hand after a failure. Administrators get live enrollment tracking, Enrollment Status Page (ESP) visibility, detailed session timelines, automated issue detection, on-demand diagnostics, fleet health analytics, and AI-assisted troubleshooting through a hosted Model Context Protocol (MCP) server.
The platform supports classic Windows Autopilot and modern Intune provisioning scenarios, including Windows Autopilot Device Preparation.
Availability
Autopilot Monitor is publicly available — the Community plan is free. Sign in with your work account at autopilotmonitor.com; new organizations complete a short activation step after first sign-in.
Key Features
- Real-time Windows Autopilot monitoring — follow device provisioning while enrollment is still running
- Detailed enrollment timelines — correlate Autopilot, ESP, Intune Management Extension, apps, policies, device state, and system events
- Enrollment Status Page visibility — see which phase, application, or policy is delaying provisioning
- Automated issue detection — define custom analysis rules that identify known failure patterns
- Targeted evidence collection — gather registry values, event log entries, files, log-parser matches, and allow-listed commands when specific conditions are detected
- On-demand diagnostics — request diagnostics packages from enrolled devices remotely
- Fleet health analytics — success rates, deployment duration, device models, applications, errors, and recurring issues
- Session reporting — inspect and share detailed enrollment information for individual devices
- Windows Autopilot Device Preparation support — monitor Device Preparation deployments alongside classic Autopilot scenarios
- Centralized agent configuration — control diagnostic behavior, automatic reboots, timezone adjustment, and other monitoring settings from the web dashboard
- AI-assisted troubleshooting — query enrollment data through the built-in Model Context Protocol server
How it works
Autopilot Monitor consists of several components working together throughout Windows enrollment:
- Bootstrap Script — PowerShell script deployed through Microsoft Intune that starts monitoring early in the provisioning 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, troubleshooting, and fleet analytics
- MCP Server — Model Context Protocol server that lets AI assistants query Autopilot Monitor data in natural language
Windows Autopilot and Intune Troubleshooting
Autopilot Monitor helps investigate common Windows provisioning and enrollment problems such as:
- Windows Autopilot deployments that fail or appear stuck
- Enrollment Status Page (ESP) delays and unusually long deployment phases
- Win32 application installation failures and slow application downloads
- Intune Management Extension problems
- Microsoft Entra join, Hybrid Join, and device registration issues
- policies that block or delay provisioning
- inconsistent behavior across hardware models and recurring failures affecting multiple devices
Because telemetry is collected during enrollment, many problems can be investigated without reproducing the issue or manually collecting logs from the affected device.
AI Integration (MCP)
Autopilot Monitor ships a hosted Model Context Protocol server. Connect Claude Desktop, VS Code, or any MCP client that speaks Streamable HTTP with OAuth, and ask questions like:
- "Show me all failed enrollments from the last 24 hours and group them by failure reason."
- "Why did session X fail, and which app install caused the delay?"
- "Which devices had unusually long ESP application phases this week?"
- "Which device models have the highest Autopilot failure rate?"
- "Which devices in my fleet are affected by CVE-2024-30078?"
Server URL: https://mcp.autopilotmonitor.com/mcp — sign-in runs through your existing work account, access is scoped to your tenant exactly like in the portal, and the MCP server stores no credentials. Setup guide: AI Integration (MCP).
Documentation
Full admin documentation is available at docs.autopilotmonitor.com — deployment, configuration, monitoring, diagnostics, troubleshooting, analysis and gather rules, integrations, and AI-assisted troubleshooting.
Open Source
Autopilot Monitor is developed openly on GitHub. The source is published so that customers and security reviewers can see what runs on their devices and with their data: the repository contains the device agent, shared library, backend, web dashboard, and MCP server.
Feedback
Bug reports and feature requests go through Issues, questions and ideas through Discussions. Security issues are reported privately, see SECURITY.md. For a problem in your own tenant, the Report Session button in the portal is the fastest path.
License
This project uses a split licensing model. The root LICENSE is AGPL-3.0, which covers the server-side components; the device-side components carry their own MIT license file:
- MIT License — Agent (
src/Agent/) and Shared library (src/Shared/) — unrestricted use on end-user devices, no copyleft obligations - AGPL-3.0 — Backend (
src/Backend/), Web Dashboard (src/Web/), and MCP Server (src/McpServer/) — modifications to server-side components remain open source, especially when deployed as a network service
The Shared library is MIT because it is a dependency of the MIT-licensed Agent. The license file inside each component directory is authoritative for that component.