From AI-Friendly Architecture to Reusable AI Migration Capability at Taobao Live

Taobao Live's recommendation application serves several core traffic scenarios. To get algorithm strategies into experiments quickly, the algorithm team originally wrote and maintained the Java UDFs, graph configurations and code branches for each scenario themselves. That shortened individual experiments, but as maintenance passed to a much smaller engineering team, multiple frameworks, branches and diagnostic paths became the dominant cost: the same public capability existed in several versions, each evolving separately and behaving differently, so a single change had to be understood, evaluated, released and verified branch by branch. The reconstruction therefore targeted the production method, not the source. Three design pillars carry it: unified artifacts with declarative differences (stable capability in code, scenario variation in orchestration), constrained flow semantics with a unified lifecycle, and structured evidence left behind by every execution — illustrated by the Chain ExecuteRemark sample. The aim is an AI-friendly architecture plus a repeatable migration capability, so agents work against explicitly observable behavior instead of inferring it from branches.

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