AI Lost in the Fragmented IDL Pile: Building a Single Context Hub with 'Unified AST'

In the previous article, we discussed the importance of semantic modeling to specify the identity of data and prevent hallucinations in AI agents (Cursor, Windsurf, etc.) or MCP protocols. However, when we try to apply this in practice, we hit the largest and coldest wall of reality: Fragmented Legacy. "The backend team uses Thrift, while the client uses old code in Protobuf. Plus, design data is scattered in Excel. When we ask an AI agent to write the entire system code, it mixes these three and makes a mess. Do we have to throw everything away and rewrite with a new standard?" Semantic modeling design started from this desperate question. Instead of breaking everything and rebuilding, an architecture was adopted: the Unified AST (Unified Abstract Syntax Tree) Hub, where all these fragmented schemas gather into one giant knowledge graph. 1. The Tragedy of Migration: AI Cannot Bridge Fragmented Contexts Many development teams attempt to migrate with the thought, "Let's move to a better