Reproducible Code Migration at Scale with AI-Generated Playbooks

This article explains how AWS Transform custom uses AI-generated playbooks to achieve reproducible code migration at scale, addressing the inherent non-determinism of LLM-based agents.

• LLM agents produce non-deterministic results; identical migration runs yield different outcomes across hundreds of interdependent decisions
• AWS Transform accumulates migration artifacts: commit histories, diffs, error logs, and decision rationales from each migration
• A four-phase multi-agent pipeline automatically generates structured migration playbooks from accumulated artifacts
• Playbook format enables natural quality filtering through distillation and makes human correction straightforward
• Playbooks organize knowledge hierarchically (chapters, sections, examples) matching migration task structure
• 77-repository playbook contained 25% more content than 10-repository version with advanced patterns and explicit workflows
• Playbook-guided planning reduced variance by 4.93% to 15.79% across three independent LLM judges
• Real example: playbook recognized throttling pattern from 143 failed attempts and embedded retry logic into future migrations

By converting raw migration artifacts into structured playbooks, AWS Transform constrains the solution space for AI planners, significantly improving consistency and reproducibility in large-scale code migrations.