Teradata Puts Data at the Core of Agentic AI with Launch of AgentBuilder

The first wave of GenAI was prompt-based. You asked a question, the model gave you an answer, and that was the extent of it. These tools could generate responses, but they operated in isolation from the systems that store and manage critical business data. Without access to real context, they often lacked the depth needed to support complex decisions or automate meaningful tasks. That is starting to change. Organizations are now looking for AI that can act with context, draw from trusted data, and deliver results without constant human guidance.

Teradata’s new AgentBuilder, announced today, is a step in that direction. It gives enterprises the tools to build intelligent agents that work directly within their data environment. By integrating with the Teradata Vantage platform and the Model Context Protocol (MCP) Server, AgentBuilder allows teams to design AI agents that are grounded in accurate information, aligned with operational goals, and capable of running across both cloud and on-prem systems. The focus is on bringing AI closer to the data so it can finally do more than just respond.

Known primarily for its work in large-scale data warehousing, Teradata has gradually expanded its platform to support more than just storage and reporting. In recent years, that expansion has included tools for machine learning, hybrid cloud deployments, and automation. AgentBuilder continues that shift, aiming to turn passive data systems into something more dynamic.

The focus is on bringing AI closer to the data so it can finally do more than just respond. “AgentBuilder represents meaningful progress in advancing agentic AI for the autonomous enterprise,” said Sumeet Arora, Chief Product Officer at Teradata.

“By combining the flexibility of open-source frameworks with Teradata’s AI and knowledge platform and our MCP Server, which provides deep semantic access to enterprise data, we’re helping organizations build intelligent agents that are not only autonomous and scalable, but also deeply aligned with their business goals, governance standards, and domain expertise.”

Arora also pointed to the value of giving teams more control over how their agents interact with data. For many organizations, being able to deploy across both cloud and on-prem environments allows sensitive information to stay where it belongs. That kind of flexibility makes it easier to align AI tools with internal policies, security expectations, and the practical realities of managing complex data systems.

AgentBuilder includes a set of prebuilt agents built for specific tasks. For example, there is one to convert natural language into SQL, helping teams extract insights without writing queries by hand. Another handles machine learning workflows, generating full pipelines from a simple prompt.

There is also a monitoring agent that tracks system health, identifies unusual behavior, and keeps performance steady in the background. These tools are designed to work independently and connect across different parts of the data environment.

One of the more standout features in the mix is the data science agent. It turns a natural language request into a functioning machine learning pipeline, covering everything from data prep to modeling to output. It relies on a combination of LLMs, Teradata’s MCP tools, and structured reasoning to follow the right sequence of steps. The goal isn’t just speed—it’s clarity. For teams working in science or research-heavy fields, the ability to trace every action taken by the model is just as important as the result it returns.

It is clear that much of the capability of this new tool depends on the MCP Server, which gives the agents access to metadata, prompt libraries, and domain-specific components. With that structure in place, agents can interact with information more precisely and avoid the kinds of errors that show up when language models work without context. Instead of relying on vague instructions, they follow defined logic tied directly to business data. That makes AgentBuilder less about building new interfaces and more about bringing reliability and control to AI systems that need to run inside real workflows.

That same architecture also opens the door to more flexibility in how these agents are built. AgentBuilder’s early release includes support for open-source frameworks like Flowise and CrewAI, with LangChain and LangGraph coming soon. These toolkits give developers modular components for shaping agent behavior.

Paired with Teradata’s existing infrastructure, they offer a foundation for building agents that do more than just perform tasks. These agents are built to adapt over time, using open source components to shape their logic while relying on Teradata’s data fabric to stay accurate and aligned with the real world. The approach is layered, with flexibility on the front end and consistency at the core.

This shift toward agentic AI is not limited to Teradata. Databricks and Snowflake have built their own frameworks for creating agents. Major cloud providers like AWS and Google are layering in tools that orchestrate how those agents interact with data and models.

Teradata is joining that movement, but its strategy looks different. Instead of rushing to be first, the focus is on making sure agents can work with trusted data and run where organizations need them most. For many, that balance between control and capability is what makes the difference between short-term experiments and long-term results – and that is exactly where Teradata hopes to stand out.

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