AI-Native Incident Management
Autoheal delivers end-to-end incident management natively — from the moment an incident is declared through resolution, RCA, and preventive action. No more stitching together separate tools for on-call, incident response, and post-mortems.
Incident Lifecycle
Incidents can be triggered from PagerDuty alerts, Datadog monitors, Grafana alerts, Sentry exceptions, Slack messages, or manual creation in the Autoheal UI.
The OnCall Agent immediately begins investigating — querying your observability stack, checking recent changes, and consulting the Production Context Graph for context.
Autoheal creates and manages dedicated Slack incident channels, pulling in the right people based on service ownership from the Production Context Graph.
Multiple team members can work with the agent simultaneously. The agent coordinates information across participants, preventing duplicate work and ensuring nothing is missed.
As the agent forms hypotheses, it routes them to the appropriate on-call engineers for validation — the database team for DB hypotheses, the platform team for infra hypotheses, etc.
Once the root cause is confirmed, the agent proposes mitigations. For code-level issues, it surfaces preventive fixes for your team to review.
After resolution, Autoheal generates a structured 5-Why root cause analysis — not a template that never gets filled out, but an actual analysis built from the investigation evidence.
The RCA feeds back into the Production Context Graph. Proposed preventive measures are tracked as action items. The same failure mode is caught faster next time.
Slack-Native Workflow
Autoheal works where your team already works — in Slack:
@Autohealin any channel to start an investigation- Dedicated incident channels created automatically with relevant stakeholders
- Real-time updates posted as the investigation progresses
- Interactive follow-ups — ask questions, redirect the investigation, request specific data
- Incident resolution and RCA delivered directly in the channel
Structured 5-Why RCA
Every incident gets a real root cause analysis, not a checkbox exercise. The RCA includes:
| Section | Content |
|---|---|
| Summary | What happened, when, and the business impact |
| Timeline | Chronological event timeline built from investigation evidence |
| 5-Why Analysis | Iterative root cause chain from symptom to underlying cause |
| Impact | Services affected, duration, user impact, SLA implications |
| Root Cause | The confirmed underlying cause with supporting evidence |
| Mitigations Applied | What was done to resolve the immediate issue |
| Preventive Measures | Action items to prevent recurrence |
Preventive Fixes
When the root cause is a code defect, Autoheal goes beyond just identifying it:
- Identifies the problematic code — pinpoints the file, function, and logic causing the issue
- Proposes a fix — generates a code change that addresses the root cause
- Surfaces the fix — presents the proposed change to your team with full context linking back to the investigation and RCA
Repeat Incident Prevention
Autoheal ensures incidents don't just get resolved — they get prevented:
- Guaranteed post-mortems — every incident gets an RCA, not just the ones someone remembers to write up
- Action item tracking — preventive measures from RCAs are tracked to completion
- Pattern detection — the Production Context Graph identifies recurring failure patterns across incidents
- Production Context Graph updates — new skills and procedures are suggested based on incident learnings
Getting Started
- Connect your alerting tools (PagerDuty, Datadog, Grafana)
- Set up Slack integration for incident channel management
- Connect GitHub or GitLab for code and deployment context
- Build your Production Context Graph with service ownership and skills