If you have ever opened an Intune diagnostics bundle and started hunting through logs by hand, the appeal is obvious. The files are there. The answer is probably in there somewhere. The slow part is knowing which file matters, which timestamp matters, and whether the first error you find is actually the cause or just noise.
Stefan Röll, a Cloud Solution Architect at Microsoft Germany, published a practical walkthrough for using GitHub Copilot CLI as a first pass through those bundles. The workflow is simple: collect diagnostics from Intune, download and extract the ZIP locally, start GitHub Copilot CLI from that folder, and ask it to inspect the relevant files, build a timeline, validate findings against Microsoft Learn MCP, and generate a root cause analysis report.
That is useful for MSP helpdesks and endpoint teams, but the framing matters. This is not a native Intune Copilot button. It is not a built-in Intune integration. It is GitHub Copilot CLI working over downloaded diagnostics and log files that an admin provides.
Where this helps
The strongest use case is messy first-pass triage. Röll’s example asks Copilot to explain why Windows 11 PCs rebooted during business hours. Instead of manually checking whether the trigger was Windows Update, a driver, an app update, a policy setting, or some other background process, the admin gives Copilot the diagnostics bundle and asks it to extract what it needs.
That can save real time. A Level 1 technician may not know every Intune, Autopilot, and Windows log path by memory. Copilot can scan across files, surface likely events, and produce a timeline that gives a senior admin a better starting point. For a busy MSP, that means fewer tickets stuck in “someone needs to read the logs” limbo.
It also changes the handoff. Instead of sending a senior engineer a ZIP file and a vague note, the helpdesk can send a generated RCA draft, the files Copilot inspected, and the timeline it thinks matters. That is easier to review than a raw bundle.
Do not skip the human check
The warning in Röll’s post should be part of the operating procedure, not a footnote: AI can make mistakes. He specifically cautions admins not to blindly trust the output, especially recommendations for how to fix an issue.
That is the right line. Copilot can help you find the likely root cause faster. It should not be the thing that decides which remediation gets pushed to client devices. If the tool says a reboot came from a policy, an update, or an app install, someone still needs to verify the evidence before changing production settings.
The Microsoft Learn MCP Server piece is important here. Röll recommends using it so Copilot can check findings against Microsoft documentation. That does not make the answer automatically correct, but it is better than asking a model to reason from logs without grounding its recommendation in current docs.
The governance angle
Microsoft’s Dataverse MCP Server update is a separate article, but it points in the same direction. The useful detail is the “tool shape”: agents get named capabilities such as search_data, read_query, create_record, and describe. Admins can reason about those capabilities, approve them, block them, audit them, and improve them over time.
That is the pattern endpoint teams should want for AI-assisted troubleshooting too. A CLI reading a local diagnostics folder is fairly contained. The risk changes when agents get closer to live tenant data or administrative actions. At that point, the question is not just “can it find the root cause?” It is also “what exactly can it read, what can it change, and who approved that tool access?”
A practical way to pilot it
Start with old tickets, not live remediation. Pick a few closed Intune or Autopilot incidents where you already know the answer. Run the extracted diagnostics through GitHub Copilot CLI and compare its RCA against what your team found manually.
Track three things: whether Copilot found the right files, whether the timeline matched the known incident, and whether its fix recommendation was safe. If it helps with the first two but gets shaky on the third, that is still valuable. You can use it as a triage assistant without letting it write the runbook.
For MSPs, the win is not “AI fixes Intune.” The win is smaller and more believable: fewer wasted minutes opening the wrong files, faster escalation notes, and a cleaner RCA draft for a human admin to validate.
Sources
- Analyse Intune Diagnostics Logs with GitHub Copilot – Stefan Röll, Microsoft Community Hub
- Dataverse MCP Server: Understanding the New Tool Shape – Microsoft Power Platform Blog
