Incident Post-Mortem Aggregator
Your agent collects every post-mortem from PagerDuty, Opsgenie, Linear, and Notion — identifies recurring root causes across quarters, categorizes by system, team, and severity, then generates a trends report. Board-ready data to justify infrastructure investments, not anecdotes.
Post-mortems get written and forgotten. The patterns stay invisible.
Every engineering org says they learn from incidents. Few actually aggregate the patterns. According to Jeli.io's 2024 Incident Analysis Report, 68% of engineering organizations have no systematic process for identifying recurring incident themes across quarters.
The result: you go to the board with "we need to invest in infrastructure" backed by gut feeling instead of data. That argument loses to the next feature request every time.
Ingests every incident source. Categorizes every root cause.
The agent connects to your post-mortem documents in Notion, Google Docs, or Confluence. It pulls incident tickets from PagerDuty, Opsgenie, and Linear. It reads Slack incident channels and on-call rotation logs. Every data source your team already uses — no new tooling required.
Each incident is categorized by root cause type, affected system, responsible team, severity level, time-to-resolve, and customer impact. The agent does the classification work your team never has time for.
A strategic trends report. Not a list of incidents.
The output is a document your board can act on. Quarter-over-quarter trends, repeat offender systems, MTTR by team, customer-impact hours — all structured to show where infrastructure investment has the highest return.
When 3 incidents trace to the same CI/CD pipeline race condition, the agent flags it with a recommendation: invest 2 sprints in pipeline hardening, projected to eliminate 35% of deployment incidents. That turns a vague ask into a quantified proposal.
When the board asks "why infrastructure instead of features?" — you have the answer.
"Why should we spend $200K on infrastructure instead of features?" Every CTO has faced this question. Without data, it becomes a request for trust. With this agent, it becomes a quantified argument backed by months of incident evidence.
147 engineering hours lost. The same root cause 8 times. A 2-sprint fix that recovers 50+ hours per quarter. That math speaks for itself — no gut feeling required.