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Diagnosing Root Cause Across Services, Infrastructure, and Recent Change
A platform team's mean time to diagnosis is dominated by the search for what changed, across deployments, configuration, and infrastructure.
Runs onSutradharHow the work runs
The pressure that made this worth automating, the steps the system runs, and what came out the other side.
Pressure & Trigger Points
- Change data lives in deployment pipelines, configuration systems, and cloud audit logs that are never correlated.
- Engineers reconstruct a timeline by hand under incident pressure.
- The same class of incident is re-diagnosed from scratch each time it recurs.
The run · 5 operational steps
Click any step to inspect telemetry signals, model reasoning, and governance gates.
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Timeline Reconstruction
Deployments, configuration changes, infrastructure events, and alerts are assembled into one ordered timeline automatically.
Input Signal:
Real-time operational telemetry & queue
Reasoning Pattern:
MCP grounded vector inference
Governance Gate:
Policy constrained with audit write-back
Verified Business Outcomes
- Incident timelines assembled automatically rather than reconstructed under pressure.
- Candidate causes ranked and tested rather than listed.
- Recurring incident classes resolved against prior diagnosis instead of from scratch.
Capabilities this relies on
- signal ingestion
- anomaly detection
- root cause reasoning
- alert routing
- autonomous execution
- workflow orchestration
- human approval
- evidence audit trail
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