Infrastructure for AI

Throughout 2026, customer feedback has led data management and analytics vendors to add capabilities that enable enterprises to develop agents that can be trusted to deliver accurate outputs so they can be put into production.

Many organizations experimented with agents dating back to 2024, but few were able to build agents trustworthy enough to deliver any return on their investments. One of the reasons many AI initiatives never made it past the pilot stage was that the data retrieval processes used to feed AI pipelines couldn't discover and deliver enough high-quality, relevant data for agents to perform as intended.

In response, vendors such as Databricks, Domo, GoodData, MongoDB, Qlik, Snowflake, Tableau and ThoughtSpot have all introduced new capabilities aimed at better enabling customers to successfully build agents rather than merely experiment with agentic AI development.

Driven by customer feedback, Teradata is similarly aiming to improve the success rate of AI development initiatives, first with capabilities introduced earlier this year and now with the Autonomous Knowledge Platform, according to Sumeet Arora, the vendor's chief product officer.

"Customer feedback was central," he said. "It came from hundreds of direct conversations with enterprises about how their relationship with data is changing -- who uses the platform, how they use it, and in what ways they need it to work differently as AI agents become part of daily operations. Those signals shaped every major element of the platform.

Specific elements of the platform include the following:

• AI Studio to provide a single place for organizations to build, deploy and govern AI tools, including an agent for hybrid data retrieval, end-to-end AI and machine learning pipelines, and model lifecycle management tools.
• Tera, an AI-powered workspace featuring a natural language interface where users can execute agentic workflows.
• Tera agents, which are prebuilt tools for specific tasks.
• Teradata Cloud, the Autonomous Knowledge Platform's first available deployment option featuring elastic compute and active compute capabilities to address the cost and performance of AI workloads and integrations with data sources to reduce data duplication.
• Teradata Factory to enable on-premises deployments for customers with data sovereignty and regulatory concerns.

"Underneath all of it is … AI moving closer to the data, not data moving to AI," Arora said. "That's an architectural principle that has shaped the platform from the ground up."

From a competitive standpoint, even as a spate of other data management and analytics providers introduce capabilities aimed at improving the AI development process, Teradata's new suite is distinguished from those of competing vendors in some ways, according to Catanzano.

In particular, he noted that capabilities which attempt to eliminate the need to choose either high performance or low cost, and either cloud or on premises, are potential differentiators. In addition, the concept of autonomous knowledge -- the delivery of business context to agents -- is significant.

"Autonomous knowledge that embeds business context, semantics and lineage directly into the platform gives agents trusted, governed understanding rather than just data access, setting it apart from vendors offering basic AI infrastructure," Catanzano said. "It seems to be a new, unique approach."

Regarding the configuration of the Autonomous Knowledge Platform, he added that it seems logically built. However, Catanzano suggested that more features that create a data and AI ecosystem through integrations would add further effectiveness.

"More clarity on real-time integration capabilities with existing enterprise systems and third-party tools would strengthen confidence in its ability to operate seamlessly across complex, heterogeneous environments," he said.

Like Catanzano, Petrie called out the value of giving users the option to deploy their AI systems on premises, noting that BARC's research shows enterprises are expressing greater concern about data sovereignty driven by regulatory mandates and US political developments.

"While not unique in the industry, the Factory option for on-prem deployments is critical," he said. "Data platform vendors must meet [data] sovereignty requirements to compete in the global arena."

In addition, Petrie noted that Teradata's new cost control and model lifecycle management capabilities help the Autonomous Knowledge Platform stand apart from competing AI development and management suites.

"Many AI adopters struggle to anticipate and measure their consumption of AI tokens, which -- as with cloud compute -- can lead to budget-breaking bills," he said. "I also like Teradata's model lifecycle management capabilities. .... The more Teradata can help data and AI teams optimize how they build, train, and iterate ML models, the better they reduce complexity and speed AI projects."

Looking ahead

After introducing the Autonomous Knowledge Platform, one of Teradata's next initiatives is to deepen the platform's capabilities to improve its ability to handle agentic AI workloads at enterprise scale, according to Arora.

In addition, adding industry-specific context for AI similar to what ThoughtSpot is doing with its domain-specific Spotter agents is part of Teradata's product development roadmap, Arora continued.

"The frame for the next six months [is] serving enterprises with agents, and agents themselves [with agents]," he said. "Both are customers of this platform."

Focusing on industry-specific agentic capabilities is wise, according to Catanzano.

 "Teradata could expand its ecosystem by developing industry-specific agent templates and prebuilt autonomous workflows tailored to verticals like healthcare, finance, and manufacturing," he said.

A marketplace for agents developed by third parties and integrations with agentic platforms would also serve the needs of Teradata's users and perhaps attract new customers, Catanzano added.

"Creating a marketplace for third-party agents and integrations would attract new users seeking rapid deployment while giving existing customers more flexibility to customize autonomous intelligence for their unique business processes," he said.

Eric Avidon is a senior news writer for Informa TechTarget and a journalist with more than three decades of experience. He covers analytics and data management.