Ataccama launched an agent-powered version of its platform on Thursday.
Now generally available, Ataccama One Agentic is designed to automate data governance and management, enabling customers to faster prepare trusted data for use in AI applications and data products.
At the core of the platform is One AI Agent, a context-aware and trained AI application to autonomously perform data worker tasks. Agents can create and apply data quality rules, detect data duplications and discrepancies, profile data, and validate results.
The agent also documents each step so that you can review the data before it is used for analysis.
By automating data quality tasks, Ataccama One Agentic aims to significantly reduce data preparation time from days to hours, allowing developers to build applications more efficiently. As a result, this is a valuable update to the vendor’s platform, said Stephen Catanzano, an analyst at Omdia, a division of Informa TechTarget.
“Ataccama One Agentic represents a significant upgrade over previous versions of Ataccama’s data trust platform,” he said. “A key advancement is the introduction of One AI Agent, which transforms the platform from a traditional rules-based system that requires manual configuration to an autonomous system that can independently learn, perform, and explain complex data tasks.”
Toronto-based Ataccama is a data management vendor specializing in data quality, providing tools to track data lineage, enable master data management, monitor data pipelines, and manage data as it moves through an enterprise’s various systems.
Ensuring trust
Trusted data is essential for developing AI applications and analytical tools.
Ataccama One Agentic represents a significant upgrade over previous versions of Ataccama’s data trust platform. A key advancement is the introduction of One AI Agent, which transforms the platform from a traditional rules-based system that requires manual configuration to an autonomous system.
Steven CatanzanoOmdia Analyst
Since the launch of ChatGPT by OpenAI in November 2022, many companies have increased their investment in AI development, showing significant improvements in GenAI technology, as generative AI (GenAI) and agents enable employees to become more informed and more efficient. In response, data management vendors have en masse created environments within their platforms designed to use their own data to train AI tools to understand an organization’s unique characteristics.
But despite growing interest in AI development, coupled with platforms built to simplify building agents and other AI tools, the overwhelming majority of AI projects (estimated at up to 80%) never make it into production.
Many factors affect failure rates. One of the main issues is data quality, or lack thereof. Without high-quality data that leads to accurate results, AI projects are doomed to failure.
Ataccama’s tools are designed to help users ensure data quality. The vendor is now automating processes with agent AI-powered automata capabilities aimed at faster and easier building the data reliability needed to develop tools that inform business decisions.
A combination of customer feedback and observing market trends gave Atacama the impetus to develop Ataccama One Agentic, said Jay Linburn, the vendor’s chief product officer.
“Customers were spending a lot of time manually fixing data issues, from creating rules and adjusting reports to debugging pipelines,” he said. “At the same time, the rise of AI has made it clear that companies need a way to trust the data that powers these systems.”
BARC US analyst Tim Grosser said the benefit of Ataccama One Agentic is that in addition to making trained data professionals more efficient, it also allows non-expert users to more broadly manipulate an organization’s data.
He pointed out that by automating processes such as defining rules and generating and testing complex data expressions from natural language prompts, business users can now perform tasks themselves that previously had to be done by data engineers or other data workers.
“These capabilities bring business users closer to the data quality process while reducing their dependence on technical teams,” Grosser said. ”[Natural language] Features… Significantly improve onboarding, troubleshooting, and productivity. Combined with an optimizer that simplifies the language and automates routine tasks, it provides a more intuitive user experience and accelerates everyday data management. ”
In addition to One AI Agent, Atacama One Agentic has the following features:
A Model Context Protocol (MCP) server that allows agents to securely access external sources such as managed data within Ataccama and large language models.
A data trust index that measures the trustworthiness of a dataset.
Reference data management keeps critical information like customer segments and regulatory categories consistent across the pipeline.
Continuous data observation capabilities to detect and resolve data quality issues.
Ataccama’s current data quality engine.
According to Catanzano, by combining One AI Agent with existing capabilities, Atacama One Agentic is streamlined to achieve its intended purpose.
He said it “seems to be logically built to achieve automation goals based on a multi-tiered architecture.”
Meanwhile, Grosser pointed out that Atacama is not the only vendor offering agent AI capabilities that address data quality. For example, Monte Carlo has agents that enable data observability, while Alation’s platform includes agents that automate data catalog functions such as governance and discovery.
However, Ataccama may be able to differentiate itself by offering reference data management, Grosser said.
“This allows organizations to base their models on consistent business language as they integrate AI with their own operational processes,” he said.
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While a lack of reliable data is one reason why many AI projects fail, another is the fear that delegating processes to autonomous AI tools can lead to mistakes that humans wouldn’t make, potentially harming the organization.
Ataccama One Agentic itself is an AI-powered platform that removes humans from certain processes. But Linburn pointed out that while agents do the mechanical work of addressing data quality, humans set intentions and review results before the data is used to train models or inform applications.
“This is a collaboration between humans and automation, with agents maintaining data quality in the background so teams can focus on analysis, policy and strategy rather than maintenance,” he said.
Catanzano similarly noted that Ataccama One Agentic’s design should ease fears about moving processes to AI by documenting each step for review and incorporating continuous observation to detect and resolve issues before they impact models or applications.
“The platform appears to address potential enterprise concerns about automated data governance through several safeguards,” he said.
next step
With the general availability of Ataccama One Agentic, Limburn said the vendor’s product development plans include adding automation with One AI Agent and expanding the new platform’s capabilities for processing unstructured data.
“We are focused on making Ataccama the data trust layer for enterprise AI, ensuring that all models, copilots, and workflows operate on data that can be explained, audited, and trusted,” he said.
Meanwhile, Catanzano suggested that Ataccama grow beyond data quality and governance by developing agents for other areas of data management.
He said, “Ataccama has the potential to extend the agent approach to predictive data management, allowing AI agents to predict data needs based on business patterns and automatically prepare datasets before they are requested.”
In addition, Ataccama could add industry-specific agent-based workflows and improve MCP server integration capabilities, Catanzano continued.
“The opportunity lies in extending the integration capabilities of MCP Server to enable it to work with a broader ecosystem of AI tools and enterprise applications, potentially making it the de-facto standard for trusted data access across multi-agent AI environments,” he said.
Eric Avidon is a senior news writer at Informa TechTarget and a journalist with over 25 years of experience. He is responsible for analysis and data management.
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