Your customer record means three different things. Your AI agents can’t tell which to trust.
Ask three systems in your enterprise what a Customer is and you’ll get three answers. The CRM has one version, the data warehouse has another, and the billing system has a third, each governed by different people, defined in slightly different ways, and rarely reconciled to the others. Your teams have lived with that for years by applying human judgment about which source to believe for which purpose. The moment you put AI agents to work across those same systems, that judgment has nowhere to live, because an agent reads whatever data it’s pointed at and treats it as true, with no instinct for which version of the truth it’s holding.
This is the problem that sits underneath every enterprise AI initiative, and it gets sharper the moment agents start working across more than one tool. Cortex CoWork, Snowflake CoCo, and the agents your teams are building all run on the data you already have, and they run across many systems at once. So the practical challenge is no longer only whether a given dataset is good. It’s whether a consistent, trustworthy version of your most important business data exists at all, and whether the trust you’ve established in one place travels with that data everywhere your AI works.
Why does trust break when data moves between tools?
Trust breaks at tool boundaries because most data quality work stays locked inside the system that produced it. A dataset gets validated, scored, and certified inside one platform, and the signal that says this data is fit to use never leaves that platform. The next tool that touches the same data starts over from nothing, and an agent operating in a different system has no way to know whether the Customer record it just pulled was the trusted one or a stale copy.
AI makes this expensive in a way that analytics never did. A human analyst moving between systems carries context in their head and notices when a number looks wrong. An agent carries nothing between systems and notices nothing, so inconsistencies that were merely annoying in a dashboard become decisions made on the wrong version of the truth, executed quickly and at scale. The effort you put into making data trustworthy only pays off if that trust is portable, readable by every tool and agent that touches the data rather than stranded in the one that produced it.
Define trust around meaning
The way through is to stop organizing trust around where data is stored and start organizing it around what the data means. A Customer, a Transaction, a Supplier, an Asset, each is a business concept your organization can define once, connect to every dataset that holds that information across systems, and govern at the level of meaning rather than at the level of a particular table. Ataccama calls these trusted data products, and the shift they represent is straightforward. You govern the definition once and apply it consistently everywhere the concept appears, instead of re-governing the same idea separately in every system that stores a version of it.
Defining trust at the level of business meaning is what makes it portable, because a trust signal attached to Customer as the business understands it can follow that concept across every platform, while a signal attached to one table cannot. This is also why shared semantic standards matter rather than being an abstraction for architects to worry about. A trust signal is only useful everywhere if every tool can read it the same way, which is why Ataccama has joined the Open Semantic Interchange as a collaborator. OSI gives trusted data products a common semantic foundation with the other tools and platforms in the ecosystem, so their meaning and their trust signal stay intact as they move rather than breaking at each handoff. Josh Klahr, who leads product management at Snowflake, framed the joint value directly, noting that consistent semantics and governance are what make data products genuinely reusable across enterprise ecosystems and that aligning on them gives organizations a stronger foundation for AI at scale.
What makes a trust signal useful to both a person and an agent?
A useful trust signal carries more than a verdict, because a pass or fail flag can’t reveal whether data cleared a meaningful bar or a trivial one. The Ataccama Data Trust Index expresses trust as a continuous score from 0 to 100 that combines data quality, ownership completeness, and business context into one explainable result, so a person or an agent can see not just whether a data product is fit to use but why it scored the way it did. That single score is what makes trust portable in practice, because it travels with the data product across tools and reads the same way to a data engineer choosing a source, an agent deciding whether to act, and an executive later asking how a conclusion was reached.
How do AI agents access trusted data inside Snowflake?
None of this requires moving data out of Snowflake, which matters because most enterprises have deliberately consolidated there and have no appetite to pull data back out to make it trustworthy. Ataccama delivers Data Trust Index signals into the Snowflake environment through its MCP Server, so agents built in Snowflake CoCo and the work business users do in Cortex CoWork can read trusted data products and their signals directly, grounding AI in verified, governed data in place. The same intelligence reaches teams through Snowflake’s Agentic Data Sharing program, where the quality scores, governance signals, and observability findings Ataccama generates surface inside Snowflake through Cortex Agents and semantic views, so teams can put that context to work without building integration layers of their own.
As Jay Limburn, Ataccama’s Chief Product Officer, put it, most enterprises already have the data they need but still lack a reliable way to make it trustworthy, interoperable, and usable wherever AI operates. When a data foundation carries a verified trust signal and works natively inside platforms like Snowflake, governance stops being oversight and becomes the infrastructure that lets AI operate with confidence across the enterprise. That is the shift worth making, because it ultimately decides whether AI delivers lasting value at scale rather than confident answers no one can vouch for.
What to take away
The same business concept exists in many systems that rarely agree, and an AI agent has no human instinct for which version to trust, so consistency has to be built into the data rather than supplied by a person.
Trust that lives inside a single tool breaks the moment data crosses into another, which makes portability as important as the quality work itself.
Defining trust around business meaning rather than physical storage is what lets a single trust signal stay valid across every system that holds the same data.
Shared semantic standards like the Open Semantic Interchange let a trust signal move across platforms without losing its meaning, keeping data interoperable across the tools you already run.
An explainable trust score serves a data engineer, an AI agent, and an executive reviewer at once, in a way a pass or fail flag never can.
Delivering trust signals natively inside Snowflake lets AI ground itself in verified data without anyone moving data out of the environment where it runs.
Give your most important data one trusted meaning
The enterprises that will trust their own AI are the ones that give their most important business data a single, consistent definition and a trust signal that travels with it everywhere AI operates. The data almost certainly already exists across your systems. The work ahead is giving it one trusted meaning and making that trust portable, so that whichever tool or agent reaches for a Customer or a Transaction, it reaches for the same trusted version and can explain why.
Ready to scale your AI initiatives with confidence? Schedule a conversation to see how Ataccama’s trusted data products and Data Trust Index signals work directly inside your Snowflake environment. We’ll map a critical business concept, such as Customer or Transaction, to a single portable definition and show exactly how you ground your AI in verified, governed data.
