Key Takeaways
- Agentic AI raises the data integrity stakes significantly. When there’s no human in the loop, bad data produces a wrong action, executed at machine speed.
- The “trust gate” pattern — a continuous integrity check that evaluates data quality, governance, and certification before an agent acts — is a practical, demonstrable solution that works inside Snowflake today.
- Closing the Agentic AI Data Integrity Gap means adding the right integrity layer on top of your existing Snowflake stack — and making sure that layer checks data continuously, not just once.
Snowflake Summit is one of those events that tends to surface the real conversations — the questions that practitioners are genuinely wrestling with as they go from AI experimentation to AI execution.
This year in San Francisco, the theme was “Making AI Real for Business.” And it couldn’t have been more fitting for what I was there to talk about. Because the question I hear most often from enterprise data and analytics teams right now is the same one I opened my session with: how do you achieve positive ROI from AI agents?
My answer, backed by a live demo running inside Snowflake Cowork, comes down to one thing: you have to get your data ready first.
Why Agentic AI Changes the Data Integrity Equation
There’s a version of this problem that organizations have lived with for a long time. Bad data flows into a dashboard. A person looks at the number, something feels off, and they go investigate. It’s not ideal, but there’s a human checkpoint.
Agentic AI removes that checkpoint. When an LLM is operating autonomously to make decisions, route workflows, and take actions, it doesn’t pause to question the input. If the data is wrong, the outcome is wrong. And it executes with confidence.
I used a specific example in my session that I think lands clearly: a sales territory agent that autonomously assigns new accounts based on billing address geography.
If those billing addresses aren’t standardized — if “Georgia” is spelled out in free text in some records and abbreviated in others, or addresses are missing directionals and zip codes — the territory logic quietly fails. Accounts get missed, some get double-assigned, and compensation disputes follow.
And on the surface, the dashboard is green. The agent ran and accounts got routed. Everything looks fine.
This is what Precisely calls the Agentic AI Data Integrity Gap: the widening divide between what Agentic AI systems are capable of delivering and what enterprise data can support with confidence. It’s not a single failure mode, but rather a set of conditions that compound. Trapped data, incomplete context, outdated records, inconsistency across systems, gaps in governance, and the cost of keeping up with it all manually.
One of the consistent themes I heard at Snowflake Summit this year was that organizations have largely moved past the question of whether to invest in AI. The question is how to operationalize it safely. And when that conversation turns to the actual data those agents will rely on, I notice that confidence tends to drop quickly.
Where Precisely Fits in the Snowflake Stack
Part of what I wanted to accomplish in my session was to make the Precisely and Snowflake partnership tangible — not just in terms of our product integrations, but where the two platforms sit relative to each other and why that matters.
Think about the Snowflake stack in layers. At the base sits the AI platform: the compute, storage, and AI runtime. Above it, Snowflake’s Horizon Catalog provides metadata and lineage. Visibility into what data you have in Snowflake and where it flows.
But between “I have data” and “I trust this data enough to let an agent act on it,” there’s a gap. That’s where Precisely comes in!
Beneath the AI runtime, Precisely is the trust foundation. The Data Integrity Suite builds a connected model of your data: quality scores, governance rules, policies, and the relationships that tie datasets to the business decisions they’re supposed to support. A living picture of which data is ready, for what, and under which conditions. That’s what makes data genuinely agent-ready before it ever reaches a workflow.
Above the AI runtime, Precisely is an access point. Through the MCP server gateway, that trust foundation is queryable by agents at the moment of decision. Think of an agent about to trigger a customer action. Before it acts, it calls Precisely, checks the quality score and governance status of the underlying dataset, and gets a real-time answer: ready or not. It’s a live signal, every time.
So Precisely isn’t a single slice in the middle of the stack. It’s the foundation trusted data is built on, and the gateway that delivers those trust signals when agents need them. Bottom-to-top wrapping the AI runtime with integrity.
The Data Integrity Suite Trust Gate: What a Live Demo Proved
The centerpiece of my session was a live demonstration of a B2B revenue advisor agent running inside Snowflake Cowork.
Here’s what played out:
I walked in as a Sales VP planning a Southeast expansion and asked the agent a simple question: What’s our customer concentration and revenue across Georgia, Florida, and the Carolinas?
Exactly the kind of question you’d want an AI agent to handle on its own.
It refused.
Not with an error — with a reason. It told me that the billing addresses in our CRM account table were inconsistent, so any regional numbers it produced would be misleading. It cited the specific thresholds:
- The CRM accounts dataset was at 79% quality, below the 90% minimum required by our AI-supported business decisions policy.
- The revenue view inherited that problem at 83%.
Neither was certified for AI use. But it also noted that the transaction data was clean. The dollars were solid, we just couldn’t trust the geography behind them.
That’s the trust gate of the Precisely Data Integrity Suite in action.
The agent doesn’t just look at the table it needs. It looks at its own registered asset in the Data Integrity Suite, follows the catalog relationship to its governing policy, reads the quality and governance thresholds for that policy, checks every dataset its use case depends on, and then decides whether to proceed.
If anything fails, it stops and explains the risk in business language, not governance jargon.
What makes this more than a one-time check is the continuous loop. After the block, we showed the remediation path: enriching a single account record through the Precisely API pipeline, which standardized the address, added county and metro area and building-level coordinates, returned exact tax jurisdiction data, and confirmed the business identity.
One messy address string in — four layers of trustworthy intelligence out.
Then, once the underlying data is remediated and re-scored, the agent’s next run passes automatically. The moment the CRM accounts table crosses the quality and governance thresholds and gets certified, the Southeast question answers itself.
Why Continuous Data Integrity Matters More Than a One-Time Check
One question I tend to hear is whether you can just certify your datasets once and move on.
The short answer is no, and it’s worth being direct about why.
The quality of your underlying data today does not guarantee the same quality tomorrow. Data changes. Records get updated, merged, or abandoned. New records come in with inconsistent formats. Systems that feed your warehouse evolve. Any governance model that treats certification as a destination rather than a continuous state will eventually produce the exact failure mode we demonstrated — an agent that passes the gate based on a stale score, then acts on data that no longer meets the threshold.
The trust gate pattern we built is designed to fire live, on every call.
If the data team remediates a table today and the scores cross the threshold, the very next question passes. If a dataset that was healthy last month has degraded, the agent blocks before an incorrect decision gets executed. That real-time evaluation is what responsible Agentic AI requires.
What I’m Thinking About After Snowflake Summit
A few things stood out to me from the broader event conversations beyond my own session.
- The Snowflake ecosystem has matured significantly around AI infrastructure — Cowork, Horizon Catalog, and the partnerships built on top of them give enterprises a genuinely strong foundation to build on. The gap isn’t in the platform layer, but the data layer beneath it.
- There’s still a real disconnect between strategic confidence and operational readiness. Leaders are bullish on AI; the teams closer to the data are asking harder questions about completeness, consistency, and governance. That gap doesn’t close by itself. It closes when organizations treat data integrity as a prerequisite for agent deployment, not an afterthought.
- Finally, and this is what I’d want anyone who attended my session to walk away with, the path from where most organizations are today to Agentic-Ready Data is more concrete and more achievable than it might feel.
That’s ultimately what Agentic AI data integrity comes down to: not a compliance checkbox, but the foundation that determines whether your agents produce outcomes you can act on and ROI you can actually measure.
You don’t rebuild your data foundation from scratch. You start with a specific use case, identify the datasets that use case depends on, strengthen the integrity layer around those datasets, prove the value, and replicate. The demo I ran at Summit was a working version of that approach. Learn more about our partnership with Snowflake and how it helps you achieve Agentic-Ready Data.
