Designing Robust AI Agent Tooling: Handling Semantic Variations Between User Language and Backend…

Designing Robust AI Agent Tooling: Handling Semantic Variations Between User Language and Backend Systems Modern AI agents often sit between human language and strict backend systems. While humans naturally use flexible, ambiguous language, backend systems - especially databases and APIs - require precise, canonical values. This is a very common (and important) problem in agent + tool design 👍 You’re essentially dealing with semantic normalization between natural language and strict backend enums. Consider an AI agent designed to fetch Flights or Hotels for a given location.