OpenClaw / OpenClaw for Incident Response

OpenClaw for Incident Response

Incidents are ideal for OpenClaw patterns because they need fast action under constraints and strong operational traceability. When a service goes down at 3 AM, an OpenClaw agent can triage the alert, execute runbook steps, and log every action -- giving your on-call engineer a head start before they even open their laptop.

Workflow example: automated incident triage

Your monitoring system (Datadog, Grafana, CloudWatch) fires an alert via webhook.
The webhook handler sends the alert payload to OpenClaw using process_failure.
OpenClaw classifies the incident severity (P1-P4) based on the affected service, error rate, and user impact.
For known incident patterns, the agent executes containment steps from your runbook (restart service, scale up replicas, toggle feature flag).
For unknown patterns, the agent proposes actions and pages the on-call engineer with a structured summary.
All actions and decisions are logged to the session, creating an automatic incident timeline.
Post-resolution, the agent generates a draft post-incident review with what was attempted, what worked, and what failed.

Code example

Report a service failure to OpenClaw for triage and containment:

curl -X POST https://api.delx.ai/v1/mcp \
  -H "Content-Type: application/json" \
  -d '{
    "jsonrpc": "2.0",
    "id": 1,
    "method": "tools/call",
    "params": {
      "name": "process_failure",
      "arguments": {
        "agent_id": "incident-responder-01",
        "error_type": "service_degradation",
        "details": "API gateway p99 latency spiked to 12s. Error rate 34%. Affected: checkout service.",
        "severity": "high"
      }
    }
  }'

High-value use cases

Auto-triage incoming alerts, propose containment actions, and coordinate repetitive runbook steps with guardrails. OpenClaw is particularly effective for the first 5-10 minutes of an incident when speed matters most and human responders are still ramping up context.

Metrics to track

Mean time to detect (MTTD) -- how quickly the agent identifies a real incident from noise.
Mean time to contain (MTTC) -- time from detection to first containment action.
Correct severity classification -- percentage of incidents where the agent's severity matches post-mortem assessment.
False positive rate -- alerts triaged as incidents that turned out to be non-issues.
Runbook completion rate -- percentage of automated runbook steps that executed successfully.

Reliability priorities

Session continuity, strict retry control, and explicit stop conditions to avoid noisy loops during unstable windows. Configure a maximum retry count and a circuit-breaker timeout so the agent does not hammer a degraded service with repeated checks.

Post-incident discipline

Log what was attempted, what worked, and what failed so future loops improve instead of repeating the same mistakes. The session timeline becomes your incident review draft -- no more reconstructing events from scattered Slack messages.

FAQ

Can OpenClaw auto-resolve incidents?

OpenClaw can execute predefined containment and recovery actions automatically for known incident types. For novel incidents, it proposes actions and waits for human approval. The process_failure tool lets you define severity thresholds that determine whether the agent acts autonomously or escalates.

How fast can agents detect degradation?

Detection speed depends on your polling interval. With a 3-minute heartbeat loop, OpenClaw agents typically detect service degradation within 3-6 minutes. For faster detection, use webhook-driven triggers that invoke the agent immediately when your monitoring system fires an alert.

Does OpenClaw integrate with PagerDuty?

OpenClaw does not have a native PagerDuty plugin, but integration is straightforward. Your PagerDuty webhook sends incident data to an intermediary service that calls OpenClaw's process_failure tool via MCP. The agent's response can then be posted back to the PagerDuty incident timeline via their API.

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