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Everyone’s racing to build with AI, but few can actually trust the data powering it. The conversation around data is changing. It’s not how much data you have, it’s how much you trust. As AI goes from pilot to production, that trust can’t be afterthought. It needs to be built into the foundation.
Ataccama today released ONE Agentic – a reimagined version of its data management platform, driven by an intelligent, autonomous AI agent. Built to further automate everything from rule generation through documentation, the platform delivers intelligence right into data governance and preparation. It adds a living trust layer that keeps enterprise data clean, explainable and ready for AI systems to act upon (not only analyze).
Ataccama reports that ONE Agentic can deliver AI-ready data up to 83% faster than traditional workflows, shortening development cycles and speeding up decision-making.
At the core of the release are two key innovations: the ONE AI Agent and the MCP Server. The ONE AI Agent autonomously detects, resolves, and documents data quality issues. This means that it removes the need for manual rule-writing and cleanup. The MCP Server connects that trusted data to AI tools like Claude and ChatGPT, exposing not just records, but rich context: where the data came from, how its quality was verified, and who can use it for what. It’s trust, machine-readable, and ready for intelligent systems to consume in real time.
The ONE AI Agent acts as an embedded data engineer, only faster, tireless and always awake. That’s at least what Atacama claims.The company says it can write the rules, find the problems, fix them, and document everything along the way. No manual cleanup, and no last-minute patchwork.
Ataccama shared it saved one team 25 work days across 1,500 assets during a real-world rollout, with rule creation and debugging as well as metadata capture handled automatically. The result? Workflows have been accelerated up to 9 times. Rather than pursuing data quality after the fact, teams now have clean, explainable data from day one. That means users extracting the data get access to what’s prepared for models, queries or AI systems that must respond in real time.
Once data is trusted internally, the challenge becomes distributing that trust to the systems that rely on it. Ataccama tackles this with its MCP Server, which wraps each dataset in a kind of digital passport. That includes where the data came from, what checks it’s passed, who’s allowed to use it, and what it’s meant for.
This extra context moves with the data into whatever system uses it next—whether it’s a tool like ChatGPT or an internal AI agent. That way, machines don’t just see numbers or text. They also understand the rules around it. A built-in Data Trust Index gives teams a clear signal of how reliable the data actually is.
“The next generation of AI will be defined by systems that act on data independently, not just analyze it,” said Jay Limburn, Chief Product Officer at Ataccama. “For years, data teams have fought fires, fixing errors after they’ve already distorted reports or slowed down projects. That reactive approach doesn’t work when AI is making decisions in real time.”
“Ataccama ONE Agentic changes this by embedding intelligence directly into how data is governed. The ONE AI Agent doesn’t just find problems; it acts on them, ensuring data stays accurate, explainable, and ready for use. It shifts the focus from managing data to trusting it, because in an AI-driven enterprise, success depends not on how much data you have but on how much you can trust.”
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Trust in data is not static, and neither are the systems on which AI relies on. If data changes without warning, trust can vanish before anyone notices. Ataccama addresses this by baking observability into the data pipeline, observing how reference data is utilized and zeroing in when it begins to drift. For example, if there’s a change in a country code on one system but not another, or when values no longer sync across apps, the platform flags this before things spiral out of control.
These small discrepancies are often where AI systems start to lose trust. However, if these issues can be identified early, teams can maintain decisions based on current, trustworthy data. It’s even more important as companies run AI across multiple systems and teams. Observability provides them a feedback loop — a means to sense when trust is waning long before the damage is widespread, and in some cases practically irreversible.
Atacama says one recent deployment reduced documentation time from weeks to hours across 100 catalog items. The platform also handled 170 rules that were automated and 47 debugging tasks solved, cutting manual data-engineering work by about a factor of 10.
As more organizations rely on AI agents that don’t just analyze but act, the bar for data trust rises. Ataccama’s approach shows what it takes to meet that bar: embedded intelligence, machine-readable governance, and observability that keeps pace with change. It serves as groundwork for AI systems that can operate independently, because they know what data to trust, and why.
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