When your AI is confident, but wrong

Every reliable system runs on a feedback loop.

Software engineers have tests, staging environments, and error monitoring - a loop that tells them when their code is breaking in production. Data engineers have observability, data tests, and lineage - a loop that tells them when a pipeline is broken or drifting. Data analysts have something more informal but equally powerful: intuition. They've seen yesterday's numbers. They know what "normal" looks like. When something feels off, they pause and investigate.

AI agents operating on enterprise data have none of this.

They reason fluently on whatever data you give them and produce confident answers. They don't check. They don't second-guess. They can confidently produce outputs that look correct but aren't.

The model is missing context for a feedback loop.

Most conversations about enterprise AI focus on the model: is it accurate enough, is it hallucinating, is it reasoning well? These matter. But a perfectly accurate model, reasoning flawlessly, will produce wrong answers when it operates on broken data.

And production data is always, at some level, broken.
Schemas change without warning. Upstream sources fail silently. Pipelines run successfully but produce the wrong values. Monitors flag anomalies that nobody has triaged yet. 

A human analyst looks at their results to see if something feels off. If so they would pause. They'd check the source. They'd look at yesterday's values. They'd ask a colleague. They’d look for context to validate the feeling, this is their feedback loop implementation. An AI agent feels nothing. It queries the data, reasons on top of it, and gives you a confident, articulate, authoritative answer. Wrong, but confident.

The missing piece isn't a better model. It's the context to run a data quality feedback loop.

What a data quality feedback loop actually looks like

A feedback loop has two components: validation tools and reference context.

Validation tools are how the system checks whether data is behaving as expected. Tests. Monitors. Anomaly detection. Freshness checks. These are the instruments the AI can invoke, or whose results it can interpret, to know: is this data in a trustworthy state right now?

Reference context is how the system understands whether a result makes sense. Historical patterns. Lineage. Recent code changes. Open incidents upstream. The normal range of a metric. What happened the last three times this pipeline ran.

Together, these form a closed loop. The AI queries data. The context tells the AI whether the data is healthy, what recent changes might affect it, and how to interpret what it's seeing. The AI makes a decision grounded in that context, or flags that something is off and doesn't proceed.

The complexity: data quality isn’t binary

It's tempting to think the fix is a metadata layer, surface some status flags to the AI and you're done. But real data quality context is more complex than a boolean.

Is a table healthy? That depends on what you're asking. It might be passing all its tests but serving stale data. It might have a resolved incident from last week that still matters for interpreting this week's numbers. It might have a freshness SLA that's met at a daily grain but broken at an hourly one. It might be trustworthy for operational reporting but not for year-over-year comparisons because a business definition changed mid-quarter.

Real data quality context is relational, historical, and situational. It needs to know what the AI is trying to do, what data it's touching, what changed recently, what's normal for this metric, what signals are currently firing, what the data actually means in business terms, and whether the AI is authorized to use it for this specific reason. It needs to be queryable - the AI needs to be able to ask, "before I trust this table for this question, tell me what I need to know."

This is what makes the data quality feedback loop infrastructure, not a feature. It's a system, and it has to be complete. A partial feedback loop, one that tells you about freshness but not quality, or incidents but not lineage, leaves the same failure modes open.

Data quality as part of the Enterprise Context Layer for AI

As the Enterprise Context Layer emerges as the foundation for production AI, the conversation has rightly focused on business semantics, lineage, governance, and access control. These matter. AI agents need to know what metrics mean, how data flows, who can see what, and what rules apply.

But none of it matters if the data itself isn't trustworthy in the moment the AI acts. This is the context that tells the AI whether the rest of the context is actually relevant.

You can have perfect semantic definitions, perfect lineage, perfect governance. If the underlying data just broke and nobody told the AI, none of it saves you. The agent will act confidently on bad inputs and produce confident, wrong outputs.

A complete Enterprise Context Layer has the data quality feedback loop built into it.

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Closing the loop

At Elementary, we've spent years building the systems data teams use to monitor, test, and observe their production data - the quality feedback loop for humans. The obvious next question is: what does that feedback loop look like when the consumer of the data isn't a human, but an AI agent?

That's the work we're doing now. Elementary's signals – data health, incidents, recent changes, anomalies, lineage context – flow into Atlan’s Enterprise Context Layer, where they join semantic meaning, governance policies, and knowledge relationships, to give AI agents the complete context they need to act reliably. The quality signal tells the agent whether to trust everything else.
We are not stopping at that - the Elementary AI agents actively and autonomously contribute to the reliability and maintenance of data and data pipelines. Imagine a team of data engineers, working 24/7, doing whatever is necessary to achieve reliable data pipelines at scale.

This is part of the vision we're building toward: rebuilding data management for the AI era, with an agentic data team and reliability as the foundation.

The broader infrastructure conversation, how the context layer comes together across categories to make production AI actually work, is happening at Atlan Activate. 

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