Monorepo Incident Root Cause Identification
AI-assisted workflow for incident responders that analyzes recent changes in large monorepos to identify the code change and owning team most likely responsible for an incident, reducing root cause investigation time.
Business Blueprint
GROUNDEDAI narrows a monorepo incident investigation from a large set of recent changes to the most likely culprit changes and owning areas for responders to review.
The Problem
Incident investigations in large monorepos become hard to scale because many teams are continuously adding changes, and responders need to identify which recent code change may be the root cause.
Investigation engineers / incident responders
They must sort through accumulating changes across many teams when investigating issues in systems dependent on monolithic repositories.
Engineers joining an investigation
They need help getting oriented to investigations and isolating root cause quickly enough to contribute.
Process Fit
Software development & deliveryAs-Is
When an incident starts, responders inspect recent monorepo changes, ownership information, and related system context to find plausible root causes. In a large monorepo, that review can span thousands of changes across many teams before the investigation has a focused shortlist.
To-Be
At investigation creation, the AI-assisted workflow retrieves a smaller set of relevant recent changes, ranks them, and presents a short list of likely culprit changes for human responders to validate before acting.
Human Checkpoints
- Responder reviews the suggested top candidate changes before treating one as the root cause. — Incident responder / investigation owner
- Low-confidence recommendations are withheld rather than shown as definitive answers. — Tool owner / investigation platform team
Systems Touched
Business Cycle
Upstream
- Recent change history and monorepo structure must be available to the investigation workflow.
- Ownership and system-impact context improve the ability to connect changes to responsible teams and affected services.
- Historical investigations with known root causes are needed to tune and evaluate the workflow for the organization’s environment.
Downstream
- Responders start with a much smaller candidate set rather than reviewing the full field of recent monorepo changes.
- Investigation handoff and escalation can begin from a ranked shortlist of likely culprit changes instead of an undifferentiated change log.
Value Evidence
- Root-cause identification accuracy at investigation creationIMPROVED
42% accuracy in identifying root causes for investigations at their creation time related to our web monorepo.
- Investigations where the true root cause appears in the suggested shortlistIMPROVED
42% of these investigations had the root cause in the top five suggested code changes.
- Initial candidate-change review burdenREDUCED
reducing the search space from thousands of changes to a few hundred without significant reduction in accuracy
- Final candidate shortlist sizeREDUCED
further reduce the search space from hundreds of potential code changes to a list of the top five.
Adoption Journey
LEVEL 1 — QUICK WIN
Gate: Prove value on backtesting against historical incidents with known root causes.
Outcome: The team can see whether AI-ranked code changes would have put the real cause into a useful responder shortlist.
LEVEL 2 — STANDARD
Gate: Prove value at investigation creation for one major monorepo or incident domain.
Outcome: Responders get ranked candidate changes early in live investigations while retaining human review.
LEVEL 4 — ENTERPRISE
Gate: Prove governance for platform-level use: confidence thresholds, approved knowledge sources, and clear human accountability for remediation.
Outcome: The capability becomes part of the incident platform, routing investigations toward likely owners while avoiding low-confidence recommendations.
Detailed per-level builds in the solution spectrum below
Risk & Governance
Low-confidence answers could distract responders or point them at the wrong change.
Posture: Use confidence measurement to detect low-confidence answers and avoid recommending them, sacrificing reach in favor of precision.
The amount of change context can exceed what the AI can evaluate at once.
Posture: Rank changes in smaller batches and aggregate results until only the final candidate set remains.
Internal code and knowledge artifacts used by the workflow need controlled access.
Posture: Limit organizational knowledge exposure to approved internal wikis, Q&A, and code sources.
Operating Intelligence
How it works
AI surfaces what is hidden in the data.
Humans do the substantive investigation.
Closed cases sharpen future detection.
Who is in control at each step
Each column marks the operating owner for that step. AI-led actions sit above the divider, human decisions and feedback loops sit below it.
Step 1
Scan
Step 2
Detect
Step 3
Assemble Evidence
Step 4
Investigate
Step 5
Act
Step 6
Feedback
AI lead
Autonomous execution
Human lead
Approval, override, feedback
AI scans and assembles evidence autonomously. Humans do the substantive investigation. Closed cases improve future scanning.
The Loop
6 steps
Scan
Scan broad data sources continuously.
Detect
Surface anomalies, links, or emerging signals.
Assemble Evidence
Pull related records into a working case file.
Investigate
Humans interpret evidence and make case judgments.
Authority gates · 1
The system is not allowed to declare the final root cause or close the investigation record without validation by the incident commander, on-call responder, or designated service owner [S1].
Why this step is human
Investigative judgment involves ambiguity, legal considerations, and stakeholder impact that require human expertise.
Act
Carry out the human-directed next step.
Feedback
Closed investigations improve future detection.
1 operating angles mapped
Operational Depth
Technologies
Technologies commonly used in Monorepo Incident Root Cause Identification implementations:
Key Players
Companies actively working on Monorepo Incident Root Cause Identification solutions: