Enterprise Agents Have a Structure Problem - Ishita Daga, Tesla
Jul 20, 2026 · 12:08
Ishita Daga, a senior machine learning engineer at Tesla, argues that enterprise agents fail because of three structural problems — ambiguity, staleness, and preference — rather than needing bigger models or more RAG. For ambiguity, she proposes a hierarchy of sources of truth: a curated semantic layer (best for known KPIs), canonical tables (parametric queries for flexibility), and a database graph (full schema but hard to maintain). To solve staleness, she recommends a context lifecycle embedding live data sources (GitHub, CRM, semantic layers) and a feedback loop that logs events, evaluates agent performance, and updates context automatically. On preference, she notes that different teams calculate the same metric differently (e.g., average milestone time by start vs. completion) and that current solutions like semantic layers or agent memory still fail to capture user-level routing, calling this an open problem requiring further research.
