Autonomous SRE

Incident Response

Configure automated incident detection, classification, and response with AI-powered root cause analysis.

Note: This guide covers an enterprise-modified version of Autohand CLI. You can fork the CLI to add these capabilities to your environment.

Overview

Modern systems generate thousands of alerts daily. Without intelligent automation, SRE teams spend most of their time triaging rather than fixing issues. Autohand's incident response system addresses this by combining real-time detection with automated remediation.

Key capabilities include:

  • Real-time incident detection and severity classification
  • Automated root cause analysis across distributed systems
  • Self-healing capabilities with human-in-the-loop controls
  • Integration with existing observability and alerting stacks

Prerequisites

Before setting up incident response, ensure you have:

  • Autohand CLI installed and configured
  • Access to your monitoring platform (Datadog, PagerDuty, or similar)
  • API keys for your cloud provider
  • A runbook repository with documented procedures
# Verify Autohand installation
autohand --version

# Check SRE module availability
autohand sre status

Monitoring integration

Connect Autohand to your monitoring stack to receive alerts and metrics:

# .autohand/sre-config.yaml
monitoring:
  datadog:
    api_key: ${DATADOG_API_KEY}
    app_key: ${DATADOG_APP_KEY}

  pagerduty:
    api_token: ${PAGERDUTY_TOKEN}
    service_id: YOUR_SERVICE_ID

alerts:
  severity_threshold: P2
  auto_acknowledge: true
  escalation_timeout: 15m

Autohand listens for incoming alerts and begins analysis when severity thresholds are met.

Incident response flow

When an incident occurs, Autohand follows a structured four-phase approach:

Phase 1: Observation

The system detects anomalies through metric analysis and trace propagation:

  • Latency histogram anomaly detection using statistical thresholds
  • Service dependency tracing via OpenTelemetry span propagation
  • Correlation analysis to identify related components

Phase 2: Diagnosis

Past incidents with similar patterns are retrieved from the vector database. The LLM generates a causal graph and labels the top suspect components.

# View diagnosis results
autohand sre diagnose --incident-id INC-2024-1234

# Output shows causal chain and confidence scores

Phase 3: Action

After checking impact against golden signal dashboards, the agent proposes remediation options. Actions can be auto-approved based on policy or sent to Slack for human confirmation.

Phase 4: Learning

Post-incident details are logged and tagged as training data. This feedback loop improves future pattern recognition and response accuracy.

Safeguards and controls

Automated incident response requires careful guardrails:

# runbooks/high-cpu.yaml
name: High CPU Response
trigger:
  metric: system.cpu.user
  threshold: "> 90%"
  duration: 5m

actions:
  - analyze_processes
  - check_recent_deployments
  - scale_if_needed

safeguards:
  require_approval: false
  max_scale_factor: 2
  cooldown_period: 10m
  dry_run_first: true

All agent actions pass through signed verification. Role-based access is enforced through zero-trust policy engines, and an immutable audit trail is stored in secure log vaults.

Deployment options

Choose how Autohand SRE runs in your environment:

  • Standalone daemon: Run as a background service on a dedicated host
  • Kubernetes operator: Deploy as a controller in your cluster
  • Serverless: Trigger via webhooks from your alerting system
# Start as daemon
autohand sre daemon --config .autohand/sre-config.yaml

# Or deploy to Kubernetes
kubectl apply -f autohand-sre-operator.yaml

Testing your setup

Validate the configuration with a dry-run incident:

# Simulate an incident
autohand sre test --incident high-cpu --dry-run

# Review the proposed actions
autohand sre logs --last 1

Always test runbooks in a staging environment before enabling production automation.