Datadog → Claude → PagerDuty: Incident Analysis Automation
Captures Datadog alerts and monitoring data, uses Claude to perform root cause analysis and suggest remediation steps, and creates enriched PagerDuty incidents. Reduces mean time to resolution with AI-assisted incident response.
Workflow Steps
Datadog
Capture monitoring alerts and correlated signals
Connect your Datadog account and configure alert forwarding for critical and warning-level monitors. Include the full alert context: affected service, metric values, historical graphs, related logs, and any correlated alerts that fired within the same time window. Pull in APM trace data and infrastructure metrics to paint a complete picture of the system state at the time of the incident.
Confluence
Retrieve relevant runbooks and past incident reports
Query your Confluence knowledge base for runbooks associated with the affected service and any past incident postmortems that match similar alert signatures. This step provides Claude with institutional knowledge about known failure modes, previous remediation steps that worked, and service-specific quirks that might explain the current behavior.
Claude
Perform root cause analysis
Send the alert data, correlated signals, and retrieved runbook context to Claude with a prompt that analyzes potential root causes, cross-references with known failure patterns in your infrastructure, suggests specific diagnostic commands to run, and recommends remediation steps ranked by likelihood of resolving the issue.
PagerDuty
Create enriched incidents
Generate a PagerDuty incident with the AI analysis attached, including the suspected root cause, recommended remediation steps, and relevant dashboard links. Set the urgency level based on the analysis, assign to the appropriate on-call engineer, and include a checklist of diagnostic steps so the responder can start investigating immediately.
Slack
Open incident channel and post real-time context
Automatically create a dedicated Slack incident channel with a standardized naming convention and invite the on-call responder, their team lead, and the SRE on duty. Post the full AI analysis, runbook links, and relevant Datadog dashboard URLs to the channel. Pin the root cause hypothesis and remediation checklist so responders have immediate context without digging through alerts.
Jira
Create follow-up ticket for post-incident review
Automatically generate a Jira ticket for the post-incident review with pre-populated fields including the timeline of events, the AI root cause analysis, the actual remediation steps taken, and a template for the five-whys analysis. Link the ticket to the PagerDuty incident and Slack channel archive so all context is easily accessible during the retrospective.
Workflow Flow
Step 1
Datadog
Capture monitoring alerts and correlated signals
Step 2
Confluence
Retrieve relevant runbooks and past incident reports
Step 3
Claude
Perform root cause analysis
Step 4
PagerDuty
Create enriched incidents
Step 5
Slack
Open incident channel and post real-time context
Step 6
Jira
Create follow-up ticket for post-incident review
Why This Works
Datadog provides comprehensive observability data but on-call engineers often need time to piece together what happened. Claude acts as an experienced SRE that instantly correlates signals and suggests the most likely causes. PagerDuty ensures the right person is notified with enough context to start resolving the issue immediately rather than spending the first 15 minutes diagnosing.
Best For
Site reliability engineers and DevOps teams who want to reduce MTTR by providing on-call responders with immediate context and suggested remediation.
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