Why AI agents need a new data stack: semantics, federation, governance
bigdata · x · 2026-11-13
The Data Exchange podcast interviews Snow Leopard founder Deepti Srivastava on why enterprise AI agents fail in production — the core issue is lack of access to live operational data.
Key points:
- Three pillars of the future AI data stack: automated semantics, live federation, and agent governance.
- Data federation historically failed, but LLMs make live federation and cross-database joins practical; warehouse latency is a real problem for agents.
- AI can auto-build semantic layers, with human feedback and tribal knowledge handling schema drift.
- MCP alone isn't enough: rogue-agent defense, data access controls, query explainability, and audit trails are required.
- Also covers role-specific semantics vs the limits of 95% accuracy, streaming vs live federation tradeoffs, and messy enterprise data / MDM in production.
Related event: AI agents need real-time data federation, not just MCP(2 posts)→
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