3 Lessons for Accelerating Foundation Model Upgrades

Migrating AI models to new versions typically takes months due to the testing, validation, and manual evaluation involved. Google Cloud's Applied ML team developed an agentic workflow to solve this problem, dramatically shortening migration time.

Through the development process, they learned three key lessons. First, by directly analyzing real migration problems, they established prompt optimization guidelines. Second, they found that traditional automation was too rigid to handle diverse data formats and edge cases. Third, real progress was made by shifting to a flexible agentic architecture. The agent dynamically performed data analysis and prompt testing while adapting to the team's specific requirements.

Real-world example: A partner team providing video subtitle translation and dubbing services had a demanding requirement: the speech duration of translated text had to exactly match the pacing of the original video. Previously, a custom fine-tuned model was essential, but using this agentic framework, they were able to migrate to the latest open-source foundation model using pure prompt engineering alone.

How to implement it: To build your own migration pipeline, you can (1) shift manual review to model-based evaluation with Autoraters, (2) build agents with the Agent Development Kit on the Gemini Enterprise Agent Platform, and (3) automate code generation and agent orchestration with Google Antigravity.