Agentic Incident Management

Autoheal handles incident management natively, from the moment an incident is declared through resolution, RCA, and preventive action. It replaces the separate tools teams usually stitch together for on-call, incident response, and post-mortems.

Incident Lifecycle

1

Incident Detection

Incidents can be triggered from PagerDuty alerts, Datadog monitors, Grafana alerts, Sentry exceptions, Slack messages, or manual creation in the Autoheal UI.

2

Automatic Investigation

The Investigation Agent immediately begins investigating. It queries your observability stack, checks recent changes, and consults the Production Context Graph for context.

3

Incident Channel Management

Autoheal creates and manages dedicated Slack incident channels, pulling in the right people based on service ownership from the Production Context Graph.

4

Parallel Team Investigation

Multiple team members can work with the agent simultaneously. The agent coordinates information across participants, preventing duplicate work and ensuring nothing is missed.

5

Root Cause Hypothesis Routing

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, and so on.

6

Resolution & Fix

Once the root cause is confirmed, the agent proposes mitigations. For code-level issues, it surfaces preventive fixes for your team to review.

7

5-Why RCA

After resolution, Autoheal generates a structured 5-Why root cause analysis built from the investigation evidence, not an empty template that never gets filled out.

8

Prevention

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:

• Mention @Autoheal in any channel to start an investigation.
• Dedicated incident channels are created automatically with relevant stakeholders.
• Real-time updates are posted as the investigation progresses.
• Interactive follow-ups let you ask questions, redirect the investigation, and request specific data.
• Incident resolution and RCA are 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 identifies it and proposes a fix:

1. Identifies the problematic code. It pinpoints the file, function, and logic causing the issue.
2. Proposes a fix. It generates a code change that addresses the root cause.
3. Surfaces the fix. It presents the proposed change to your team with full context linking back to the investigation and RCA.

Repeat Incident Prevention

Autoheal works to prevent incidents, not just resolve them:

• 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.

Get Started

1. Connect your alerting tools (PagerDuty, Datadog, Grafana)
2. Set up Slack integration for incident channel management
3. Connect GitHub or GitLab for code and deployment context
4. Build your Production Context Graph with service ownership and skills