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Governed Agents: The foundation for enterprise Al.

Why metric trust is the gating factor for putting AI to work on your business data — and how OpenAI, AtScale, and Snowflake solve it together.

AtScale | Snowflake | OpenAI

 

THE PROBLEM

Enterprise leaders want to put generative AI on their business data. They can’t — because agents hallucinate metrics, invent definitions, and produce confident answers that don’t match the dashboards leadership already trusts. The blocker isn’t the model. It’s the missing layer of governed business context between the data and the agent.

THE ARCHITECTURE

Architecture diagram: question > OpenAI > AtScale > Snowflake

An OpenAI agent answers a business question by reasoning over metrics governed by AtScale’s semantic layer, served from live data in Snowflake. The agent doesn’t redefine the company’s numbers — it uses the source of truth the business already trusts.

“At WPP, we meet our clients where they are and help them deliver value from trusted AI. We’re excited about what Snowflake, AtScale, and OpenAI are doing together — combining data, governed semantics, and reasoning to give agents the foundation enterprises can actually trust to put AI to work in their business.”

— Shane McAndrew, CEO, North America, Choreograph (WPP)

How it works

A campaign performance scenario — the workload every CMO, CFO, and analyst needs to trust.

THE USE CASE

The Challenge
Marketing leaders ask agents questions like “Why is ROAS down on connected TV?” Without governance, the agent calculates ROAS its own way — producing a number that doesn’t match the CMO’s dashboard. Trust collapses. The agent gets abandoned.

The Solution
With AtScale’s semantic layer between OpenAI and Snowflake, the agent uses the same metric definitions the company has already approved. Every answer is traceable to a governed definition. The agent isn’t a separate source of truth — it’s an interface to the source of truth the business already trusts.

What Enterprises Get
Agents that answer KPI-heavy questions with the same numbers as the dashboard. Auditable definitions for every metric. A foundation that scales from one workflow today to dozens tomorrow without rewriting the governance underneath.

Three Reasons Why This Matters Now

    1. Metric trust is the gating factor
      Reasoning quality means nothing if the inputs are wrong. Governance isn’t a feature – it’s the prerequisite for production Al.
    2. Governance lives in the semantic layer
      Prompting agents to “use
      the right number” doesn’t scale and isn’t auditable. The definition has to live where the business owns it.
    3. The architecture is open
      Built on platforms enterprises already run – Snowflake for data, AtScale for semantics, OpenAl for reasoning. No rip-and
Three Reasons Why This Matters Now

1. Metric trust is the gating factor
Reasoning quality means nothing if the inputs are wrong. Governance isn’t a feature — it’s the prerequisite for production AI.

2. Governance lives in the semantic layer
Prompting agents to “use the right number” doesn’t scale and isn’t auditable. The definition has to live where the business owns it.

3. The architecture is open
Built on platforms enterprises already run — Snowflake for data, AtScale for semantics, OpenAI for reasoning. No rip-and-replace.

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