Single-prompt AI refactoring fails at scale due to context window limits and coherence degradation. The 'model handshake' pattern solves this by chaining specialized AI agents (Analyst, Architect, Implementer, Reviewer) where each stage consumes a validated JSON schema from the previous stage and produces a schema-conformant output for the next. The post covers full architecture design, handoff contract schemas, a Python orchestration script using Claude and GPT-4o, validation gates with Pydantic and jsonschema, retry/error-recovery strategies, and a real-world walkthrough migrating a 40-file Express.js API to Fastify. Limitations include overhead for small refactors, multiplied API costs, and inherent non-determinism.
Table of contents
Table of ContentsWhy Single-Prompt Refactoring Breaks Down at ScaleThe Model Handshake Pattern: Architecture OverviewBuilding the Handoff ContractOrchestrating the PipelineError Recovery and Self-Healing StrategiesReal-World Walkthrough: Migrating an Express.js API to FastifyPitfalls, Limitations, and When Not to ChainSort: