Best Practices Intermediate

Deploying AI-assisted Ansible automation in production requires careful attention to safety, reliability, and team workflows. This lesson covers the essential best practices gathered from real-world deployments.

Prompt Engineering for Network Automation

Effective prompts are the foundation of high-quality AI-generated playbooks. Follow these guidelines:

Testing and Validation Strategy

1. AI validation pass

   Submit the generated playbook to AI for security and compliance review before any execution.

2. Static analysis

   Run ansible-lint and yamllint to catch syntax and style issues.

3. Dry run (check mode)

   Execute with --check --diff against lab devices to preview changes without applying them.

4. Lab deployment

   Run against lab or staging network devices to verify actual behavior.

5. Canary deployment

   Apply to a small subset of production devices before full rollout.

6. Full deployment

   Roll out to all target devices with monitoring and automatic rollback on failure.

Security Considerations

Team Workflow Recommendations

Common Mistakes to Avoid

• Blind trust in AI output - Always review and test generated playbooks
• Skipping backup tasks - Every playbook that modifies config should backup first
• Missing error handling - Use block/rescue/always for critical operations
• Ignoring idempotency - Ensure playbooks can run safely multiple times
• Over-automation - Not everything should be automated; some changes need human judgment

Measuring Success