Your Modern AI Stack Needs a Data Trust Layer  and a Context Layer 

AI agents don’t pause when they encounter bad data. They act on it, forcing enterprises to rethink their data architecture design.

Catching bad data before it impacted a downstream dashboard or decision was simpler when humans controlled the whole system. Though still prone to manual error, an analyst could catch a duplicate, adjust for a stale value, or correct an inconsistency before the data informed an action. Agentic AI removes that safeguard and means that incorrect actions could be taken at a larger scale, wreaking havoc downstream that is detected too late.. Data that was ‘good enough’ for a dashboard becomes a direct liability when it’s powering an agent, especially if that agent is feeding a pricing model, risk assessment, or compliance report. Many organizations are only now trying to figure out how they avoid the cost of getting this wrong. 

Ataccama Data Trust Report found that only 33% of organizations report meaningful progress on AI readiness, while 68% of CDOs rank data quality as their top concern. That gap is architectural, not aspirational. The semantic layer defines what data means, unifies business definitions, aligns metrics, and gives agents a shared vocabulary to reason across the enterprise. That is genuinely valuable, but it does not verify that the underlying data is actually correct. Feed it incomplete records or inconsistently defined entities, and you get structured, confidently wrong answers. 

Ataccama exists to close that gap, operating as the data trust layer that validates, governs, and certifies data before it reaches any agent or downstream system. It also ensures that, as businesses modernize their data and move it into lakehouses like Snowflake or Databricks, they can ensure that data entering these lakehouses starts clean and stays trusted, wherever it flows.  

Data from Ataccama can move into Atlan’s Enterprise Context Layer, surfacing organizational knowledge that no single system holds to AI agents. Teams have refined the definition of “enterprise customer” over many years and have built the revenue logic, often over decades of quarterly closes. Atlan provides that context to agents so they not only know what a metric is called, but also understand what it means. However, if that meaning relies on unvalidated data, the context itself becomes unreliable. Ataccama and Atlan work together precisely because both problems need to be solved, and in the right order. 

Ataccama operates across the full data lifecycle, not just at the point of consumption. Before data reaches a semantic view or an agent acts on a context product, Ataccama has already profiled it at the source, identifying anomalies, gaps, and inconsistencies that would otherwise propagate silently downstream. Finance, compliance, and operations teams define quality rules, and the system enforces them continuously, applying them as a standard at ingestion, transformation, and delivery rather than as a one-time audit. This means fewer pipeline failures, cleaner regulatory reporting, and AI programs that maintain the confidence of the teams depending on them. 

Ataccama does not just flag issues when it finds them. It remediates them by standardizing formats, resolving duplicate entities, enriching incomplete records, and routing exceptions that require human review through governed workflows. The result is that core concepts, from customer to product to counterparty to policy, are consistently defined and verified wherever they appear across the data estate. That consistency at the data layer translates directly into reliability at the context layer, allowing agents to reason across domains and return answers the business can act on.

The Data Trust Index provides a machine-readable, real-time signal that any agent or system can check before acting on a dataset. If data does not meet the thresholds defined by the business, it does not move forward. The gate is automated, auditable, and enforced before execution, not after. For the teams consuming those outputs, that means fewer escalations, faster decisions, and AI results they can stand behind in a boardroom or regulatory review. 

The semantic layer defines meaning. Atlan’s Enterprise Context Layer makes that meaning queryable by every agent and system that needs to act. Ataccama certifies that the data beneath both is actually correct, continuously, and before anything downstream depends on it. The trust layer is not an enhancement to the modern AI stack. It is the precondition for everything else to work reliably and for AI to deliver the business outcomes it was adopted to produce.