IncidentLens
AI-powered incident analysis system that processes logs and alerts asynchronously to generate root cause summaries.
Overview
An incident analysis system for on-call workflows that accepts incident context quickly and performs heavier reasoning asynchronously in the background.
Problem
Production incidents are high-pressure moments, and manual investigation across logs, alerts, and fragmented context slows down triage. A useful system here cannot block the intake path or make responsiveness depend on expensive analysis.
Approach
IncidentLens accepts incident data immediately, queues the work, and lets a background worker process logs and alerts through an LLM-based reasoning flow. That split keeps ingestion fast while still producing a structured summary for engineers once analysis completes.
Engineering Decisions
The most important design choices behind the project and why they matter.
Decoupled ingestion from analysis
The API accepts incidents immediately and hands work to an asynchronous pipeline so heavy reasoning does not sit on the critical request path.
Optimized for incident pressure
The design assumes operators need responsiveness first and rich analysis second, which is a better fit for real production triage than synchronous processing.
Returned structured summaries
The analysis output is shaped as a root-cause summary rather than raw model text, making it easier to consume during stressful operational work.