Oliver Kieselbach
fef09690d2
Agent uploads arrive as bursts of batches ~0.3 s apart with seconds of silence in between, and every batch raises a SignalR signal. The 300 ms trailing debounce on the session page either refetched once per batch or hid a tight burst until it paused; session 46096bcd produced 155 full first-page reads for 261 batches in 13 minutes. lib/burstScheduler: leading edge + trailing debounce + max wait. The first signal after a quiet period refetches at once, later signals of a burst refetch once after the burst's last signal, and a burst that never pauses still refetches every max-wait (spool replay after reconnect/reboot). Fetch semantics are unchanged: every run reads the full first page, the server stays the single source of truth, no incremental path. Session page: trailing 300 ms, max wait 1 s. Progress portal (same pattern, two requests per refresh): trailing 500 ms, max wait 1.5 s. scheduleFetchEvents loses its delayMs parameter; the scheduler is created lazily on the first signal so refs stay out of render (react-hooks/refs). Seven fake-timer tests pin the timing rules. Co-Authored-By: Claude Fable 5.1 <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.
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.
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
- MCP Server — Model Context Protocol server that lets AI assistants query and troubleshoot enrollments in natural language
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 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 no credentials are stored. Setup guide: AI Integration (MCP).
Documentation
Full admin documentation is available at docs.autopilotmonitor.com
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.