AI agents in software delivery checklist

1. Secure executive sponsor and publish acceptable-use policy (security, privacy, IP, audit logging).
2. Inventory candidate workflows; score them on impact, frequency, and risk. Choose one or two to pilot.
3. Provision agent runtime and bot accounts with least privilege; enable logging of every action and prompt.
4. Document baseline metrics (cycle time, PR review time, escaped defects, developer sentiment).
5. Define human-in-the-loop approach: approval workflows, escalation paths, and rollback strategies for automated changes.
6. Implement first workflow end-to-end, storing prompts, config, and evaluation scripts in Git.
7. Run pilot for 2–4 weeks, collecting weekly feedback surveys and comparing metrics to baseline.
8. Address safety findings—update prompts, tighten permissions, or add validation tests before broadening scope.
9. Publish results, update documentation, and decide whether to scale, iterate, or sunset the workflow.
10. Schedule quarterly audits of prompt versions, access logs, and compliance with policy.

Prerequisites

• Legal and security review of data flows and vendor terms.
• Observability into delivery metrics to detect regressions.

Pitfalls

• Launching too many workflows at once, diluting focus and measurement.
• Allowing prompt drift without code review or version control.
• Ignoring developer sentiment, leading to shadow AI experiments.

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