Enterprise Agents Have a Structure Problem - Ishita Daga, Tesla

Most enterprise agents fail for the same reason: the model can generate SQL, call tools, and follow workflows, but it has no understanding of how the business actually defines its data. Most teams try to fix this with longer prompts, more RAG, or a bigger model. The real fix is building semantic retrieval infrastructure — a machine-readable metadata layer that lets agents reason over business concepts instead of guessing at raw schemas.

In this talk, I’ll walk through how this metadata powers business-context-aware agents through semantic retrieval, metadata graphs, and domain-specific sub-agents.

Speakers:
- Ishita Daga (Tesla): Ishita Daga is a Senior Machine Learning Engineer at Tesla, building enterprise AI systems that combine semantic retrieval, metadata infrastructure, and business-aware reasoning to make analytics agents reliable in production. Previously, she was an AI Scientist at Covera Health, where she built multimodal and weakly supervised ML systems for radiology intelligence. Her interests include enterprise agent architectures, semantic layers, and operational AI systems.