Trusted Context,
Fewer Tokens

The hidden cost of ungoverned data: agents that burn tokens, drift, and break trust.

Enterprise data is still siloed, fragmented, and ungoverned, and agentic AI makes every crack in that foundation expensive. Instead of retrieving business-ready answers as efficiently as possible, agents discover, retrieve, join, filter, and reconcile data across fragmented sources, consuming tokens on work the data platform should perform. The result is predictable: more tokens, more latency, more retries, and, because they have to sift through larger amounts of unapproved data, less trust.

The answer is an AI data layer that pushes data work back where it belongs and delivers active context: real-time, governed, semantically-rich data without moving or copying it.

Inside, you'll discover:

• The four structural sources of wasted work — and token cost — in agentic AI
• Why LLMs shouldn't perform joins, filtering, and governance that belong in the data layer
• How real-time, governed, semantic data cuts retrieval, retries, and cost at once, while also improving trust

You can't build smart agents on dumb data. See the architecture behind lower cost and higher trust.