Automate·Advanced·50 min·Updated Sep 30, 2026

Build a root-cause analysis agent for recurring IT incidents

Build a Copilot Studio agent that analyzes recurring incident tickets to identify the underlying cause and recommend a proactive fix instead of repeated firefighting.

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Microsoft 365

Works With

Prerequisites

A ticket history export covering a meaningful window of recurring incidents

Business Outcome

Fewer repeat incidents of the same type because the underlying cause gets fixed instead of just the symptom.

Workflow Overview

The apps run in this order.

SharePoint
Teams
Copilot Studio

Step 1: Identify the recurring pattern firstSharePoint

Export the ticket history into Excel as a Table and use a PivotTable to group by symptom, affected system, and time-of-day/week — you're looking for a cluster, not a single pattern-matching keyword.

Confirm there's an actual recurring shape (same symptom, similar timing) before handing it to the agent; feeding it a handful of unrelated tickets just because they share a vague symptom produces a hypothesis built on noise.

Step 2: Build the analysis agentCopilot Studio

In Copilot Studio, feed the agent the grouped ticket history — descriptions, timestamps, affected systems, resolution notes — and have it propose a root-cause hypothesis with the specific evidence from the tickets that supports it.

Prompt idea:

These 14 tickets over the past 6 weeks all report the same VPN disconnect symptom, clustering around 9am each weekday. Based on the ticket details and resolution notes, what's the most likely root cause, and what evidence points to it?

Step 3: Recommend, don't auto-remediateTeams

Have the agent propose a fix (a config change, a capacity increase, a patch) for an engineer to evaluate and implement — this agent's job is diagnosis, not making infrastructure changes unsupervised.

Step 4: Track whether the fix actually resolved the patternSharePoint

Set a calendar reminder for 2-3 weeks after the fix ships (don't rely on remembering) and re-pull the same ticket grouping from step 1.

If the pattern is genuinely gone, log the fix and its evidence as a confirmed case; if it's still occurring at a lower rate, that's a different finding than "resolved" and should be logged as such.

Check the work

  • Confirm the evidence cited (ticket numbers, timestamps, resolution notes) actually says what the agent claims — check a few directly.
  • Watch for the agent mistaking correlation (same time of day) for causation — have an engineer sanity-check the proposed mechanism.
  • Verify the recurring pattern didn't stop for an unrelated reason before crediting the fix.

Source: Microsoft Copilot Scenario Library — IT (2026)

Expected Outcome

A written root-cause hypothesis for each recurring incident pattern, with supporting evidence and a recommended fix, ready for engineering review.

✓

AI is the right call here

Grouping tickets by symptom and timing (step 1) is a pivot table. Turning resolution notes and timestamps across 14 tickets into a plausible root-cause hypothesis is causal reasoning over text — exactly where a fixed rule falls short.

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