How to Recover After an AI Agent Modified Production

When an AI agent deploys code, modifies configurations, runs migrations, or makes any change to your production environment without authorization, you are in an active incident. SafeClaw by Authensor prevents this by blocking all production-related actions — deployments, production branch pushes, infrastructure commands, and production database access — through deny-by-default gating. If production has already been modified, you need to assess impact, roll back, and lock down access immediately.

Incident Response: First 30 Minutes

1. Assess Whether Production Is Down or Degraded

Check your monitoring immediately:

• Is the application responding?
• Are error rates elevated?
• Are users affected?
• Is the database healthy?

2. Stop the Agent

Terminate the agent process immediately to prevent further production changes.

3. Immediate Rollback

If the agent deployed code via CI/CD:

# Roll back to previous deployment (platform-specific)

Vercel
vercel rollback

AWS ECS
aws ecs update-service --cluster prod --service app --task-definition app:PREVIOUS_VERSION

Kubernetes
kubectl rollout undo deployment/app -n production

Heroku
heroku releases:rollback -a your-app

Docker Compose
docker-compose -f docker-compose.prod.yml down
docker-compose -f docker-compose.prod.yml up -d  # with previous image tag

If the agent pushed to the production branch:

git log --oneline -10 origin/production
git revert <agent-commit-hash>
git push origin production
Trigger deployment of the reverted state

If the agent modified production config or environment variables:

• Restore previous values from your config management system
• If using cloud providers, check the revision history in the console

4. Verify Recovery

After rollback, verify production is healthy:

• Check application health endpoints
• Monitor error rates for 10-15 minutes
• Verify critical user flows work
• Check database integrity

Investigate What Changed

Review SafeClaw Audit Log

npx @authensor/safeclaw audit --filter "action:shell.exec" --last 50
npx @authensor/safeclaw audit --filter "action:git.push" --last 20
npx @authensor/safeclaw audit --filter "action:network.request" --last 30

The hash-chained audit trail provides a tamper-proof record of every action the agent took.

Check Deployment Logs

• Review your CI/CD pipeline logs for unexpected deployments
• Check cloud provider activity logs (AWS CloudTrail, GCP Audit Logs)
• Review git history for the production branch

Document the Incident

Record:

• Timeline of events


• What the agent changed


• Impact on users


• Recovery actions taken


• Root cause (how the agent gained production access)

Install SafeClaw and Block Production Access

npx @authensor/safeclaw

Configure Production Protection Policies

Add to your safeclaw.policy.yaml:

rules:
  # Block all production branch operations
  - action: git.push
    resource: "production"
    effect: deny
    reason: "Agents cannot push to production"

- action: git.push
    resource: "main"
    effect: deny
    reason: "Agents cannot push to main"

# Block deployment commands
  - action: shell.exec
    resource: "vercel --prod*"
    effect: deny
    reason: "Production deployments require human action"

- action: shell.exec
    resource: "kubectl  -n production"
    effect: deny
    reason: "Production k8s operations blocked"

- action: shell.exec
    resource: "aws ecs update-service*"
    effect: deny
    reason: "ECS service updates blocked"

- action: shell.exec
    resource: "docker-composeprod"
    effect: deny
    reason: "Production docker operations blocked"

# Block production environment variables
  - action: file.read
    resource: "*/.env.production"
    effect: deny
    reason: "Production env files are off limits"

# Block production database access
  - action: network.request
    resource: "productiondatabase*"
    effect: deny
    reason: "Production database access blocked"

# Allow staging/development only
  - action: shell.exec
    resource: "vercel --env preview*"
    effect: allow
    reason: "Agent can deploy to preview environments"

- action: git.push
    resource: "feature/**"
    effect: allow
    reason: "Agent can push to feature branches"

Separate Agent Credentials from Production

Never give agents credentials that have production access:

rules:
  - action: file.read
    resource: "*/kubeconfig"
    effect: deny
    reason: "K8s config files blocked"

- action: file.read
    resource: "**/.aws/credentials"
    effect: deny
    reason: "AWS credentials blocked"

Prevention Checklist

1. Agents should never have production credentials — use separate, limited-scope credentials.
2. Block all production branches and deployment commands in your SafeClaw policy.
3. Use environment-based policies so agents only operate in development/staging.
4. Require human approval for any production-adjacent action.
5. Review audit logs after every agent session.

Related Resources

• AI Agent Broke the Build: Recovery and Prevention
• How to Recover After an AI Agent Broke Your CI Pipeline
• Scenario: Agent Pushed to Production
• Workflow: Staging Environment for AI