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Strategy and Analysis

Accelerate incident response and post-mortems

When PagerDuty fires, AirOps MCP immediately aggregates context: correlated Datadog alerts, recent deployments from GitHub, relevant runbooks from Confluence, and the last 24 hours of Slack conversation in the affected service channel. Within 60 seconds, on-call engineers receive a ranked list of probable root causes with supporting evidence. After resolution, the workflow assembles a post-mortem draft — timeline, contributing factors, and action items — ready for review.

Process

#1

Connect your alerting stack

Link PagerDuty to AirOps. The workflow triggers automatically when an incident is created, using the incident metadata to scope the investigation.

#2

Aggregate incident context

AirOps pulls correlated Datadog alerts, recent GitHub deploys, relevant Confluence runbooks, and the Slack thread from the affected service channel.

#3

Generate root cause hypotheses

The workflow analyzes the aggregated context and produces a ranked list of probable root causes, each with supporting evidence from the data.

#4

Assist live investigation

During the incident, engineers can prompt AirOps directly in Slack to query logs, check runbooks, or draft customer communications.

#5

Auto-draft the post-mortem

After resolution, AirOps assembles a post-mortem draft: timeline, contributing factors, impact summary, and action items — ready for the team to review.

60% faster mean time to resolution (MTTR); Automated context aggregation across 5+ tools; Root-cause hypotheses ranked by supporting evidence; Post-mortem drafts generated in minutes, not hours

Key benefits

Publish 10,000+ pages from a single workflow run

60% faster mean time to resolution (MTTR); Automated context aggregation across 5+ tools; Root-cause hypotheses ranked by supporting evidence; Post-mortem drafts generated in minutes, not hours

Connected tools

Ahrefs
Datadog; PagerDuty; GitHub; Slack; Confluence
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Try this use case
engineering-code-review; support-ticket-routing; data-anomaly-detection

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