Why Integrations Matter for RCA
Traditional incident response requires engineers to manually search through multiple tools - checking logs in one place, metrics in another, and deployment history somewhere else. This context switching wastes precious time during outages and increases the chance of missing critical signals. Steadwing integrations solve this by automatically pulling data from all your connected tools and correlating it in real-time. When an incident occurs, Steadwing already has the full picture - no manual searching required.How Integrations Power Root Cause Analysis
Steadwing’s AI correlates data from multiple sources simultaneously to identify root causes that would take humans hours to find manually.Faster Resolution
Reduce MTTR by eliminating manual log searches and metric lookups. Steadwing queries all your tools in parallel during analysis.
Smarter Correlation
AI identifies patterns across logs, metrics, code changes, and deployments that humans often miss when investigating separately.
Complete Context
Every RCA includes evidence from all connected tools - no more switching between dashboards to piece together what happened.
Continuous Learning
Each incident improves Steadwing’s understanding of your system, making future analyses faster and more accurate.
What Steadwing Correlates
During root cause analysis, Steadwing automatically pulls and correlates:| Data Type | What Steadwing Looks For | Example Sources |
|---|---|---|
| Logs | Error messages, stack traces, anomalies | CloudWatch, GCP, Elasticsearch, Mezmo, Loki |
| Metrics | Performance degradation, resource spikes | Datadog, New Relic, Prometheus, SigNoz |
| Errors | Exceptions, crash reports, error rates | Sentry |
| Traces | Request flows, latency bottlenecks | GCP Cloud Trace, SigNoz |
| Code Changes | Recent commits, deployments, releases | GitHub, GitLab |
| CI/CD Pipelines | Pipeline failures, build status, deployment events | GitLab, Vercel |
| Infrastructure | Pod status, cluster events, resource usage | Kubernetes, AWS, GCP |
| Sandbox Execution | Sandbox logs, lifecycle events, resource metrics | E2B |
| Deployments | Build status, serverless functions, edge config | Vercel |
| Alerts | Triggered monitors, alarm states | Datadog, CloudWatch, SigNoz |
| Incidents | Incident timelines, escalations, response metrics | PagerDuty, Better Stack |
| Uptime | Monitor states, response times, heartbeat status | Better Stack |
| Network | DNS records, WAF rules, firewall events | Cloudflare |
The RCA Process with Integrations
- Alert Received - An incident is triggered via Slack, Linear, or your monitoring tools
- Data Collection - Steadwing queries all connected integrations for relevant data around the incident timeframe
- Signal Correlation - AI analyzes logs, metrics, errors, and code changes together to find patterns
- Root Cause Identification - Steadwing identifies the most likely cause with supporting evidence from multiple sources
- Solution Proposal - Actionable remediation steps are suggested based on the identified root cause
Integration Categories
Steadwing supports integrations across your entire stack:- Communication - Receive alerts and trigger RCA directly from Slack
- Issue Tracking - Analyze issues assigned in Linear automatically
- Incident Management - Pull incident timelines, alerts, and response metrics from PagerDuty
- Code & Deployments - Correlate incidents with recent GitHub and GitLab commits, merge requests, and releases
- CI/CD Pipelines - Monitor GitLab pipeline failures and deployment events
- Uptime & On-Call - Pull monitor states, heartbeat status, and on-call schedules from Better Stack
- Observability - Query metrics and APM data from Datadog, New Relic, SigNoz, and Grafana
- Error Tracking - Pull error details and stack traces from Sentry
- Logging - Search logs from Elasticsearch, Mezmo, CloudWatch, and GCP
- Infrastructure - Monitor Kubernetes clusters, AWS, GCP, Cloudflare, Supabase, Vercel, and E2B
Getting Started
- Navigate to Settings in your Steadwing dashboard
- Connect the integrations relevant to your stack
- Start triggering RCA from Slack or Linear - Steadwing handles the rest
Custom Instructions
Custom instructions let you provide additional context to Steadwing’s RCA analysis, helping the AI gather more relevant evidence from your specific environment. They are available at two levels — organization-wide and per-integration. You can configure custom instructions from Settings in your Steadwing dashboard.Only organization owners can update custom instructions. Any member can view them.
Organization-Level
Organization-level instructions are applied to the main RCA orchestrator prompt. Use these to describe broad context about your system — service architecture, team ownership, naming conventions, or known dependencies. Examples:- “Our payment service is called billing-core and lives in the payments repo”
- “Deployments happen via GitHub Actions — check the deploy-prod workflow for recent runs”
Integration-Level
Integration-level instructions are applied to the specific subagent prompt for that integration (e.g., Datadog, Sentry, GitHub). Use these for platform-specific context like log group names, dashboard IDs, repository mappings, or query filters. Examples:- “Always check the prod-alerts Datadog dashboard for latency spikes”
- “Our Sentry org slug is acme-corp and errors are tagged by team with
team:platform”