TLDR: Teradata has launched AgentBuilder, a new suite of capabilities designed to accelerate the development and deployment of autonomous AI agents. This platform aims to help enterprises build intelligent agents that leverage trusted data, advanced analytics, and hybrid infrastructure to perform tasks like churn analysis and system monitoring, addressing common challenges in operationalizing AI.
SAN DIEGO – Teradata (NYSE: TDC), a prominent enterprise data analytics company, today announced the release of AgentBuilder, a comprehensive suite of tools aimed at empowering businesses to develop and deploy autonomous AI agents with enhanced contextual intelligence and domain expertise. This strategic move positions Teradata at the forefront of the evolving AI landscape, enabling organizations to transition from experimental AI agent implementations to production-ready systems.
AgentBuilder is engineered to tackle significant hurdles in operationalizing AI agents, such as data fragmentation leading to unreliable outputs, a lack of embedded business context, performance bottlenecks, inflated costs associated with prompt-driven workloads, and insufficient governance. By integrating contextual knowledge, deep domain expertise, and scalable hybrid infrastructure, the platform allows enterprises to build and manage multiple agents that can operate securely and autonomously across both cloud and on-premises environments.
Key features of AgentBuilder include its leverage of open-source frameworks like Flowise and CrewAI, with future support planned for LangChain and LangGraph. The platform is powered by Teradata’s robust AI and knowledge platform, which incorporates its recently launched Model Context Protocol (MCP) Server. A notable inclusion is the provision of ‘Teradata Agents’ – pre-built, task-driven templates designed to accelerate implementation and deliver rapid impact for complex, domain-specific challenges. Examples of these specialized agents include a SQL agent for natural language to database query conversion, a data science agent for creating machine learning pipelines, and a monitoring agent for overseeing database systems.
Sumeet Arora, Chief Product Officer at Teradata, emphasized the significance of this launch, stating, “AgentBuilder represents meaningful progress in advancing agentic AI for the autonomous enterprise.” He added that the platform allows users to build agents directly where the data and knowledge reside within Teradata, ensuring that AI and knowledge remain integrated.
Michael Ni, an analyst at Constellation Research, commented on the offering, noting, “For existing Teradata customers, AgentBuilder adds a strategic bridge from analytics to action. It’s a pragmatic extension that builds on Teradata’s customers’ needs and gives them a governed way to extend their deep understanding of enterprise and industry models, and to deploy AI agents building on the customers’ own data and infrastructure.” Ni further described AgentBuilder as ‘evolutionary more than disruptive,’ highlighting its relevance to target buyers concerned with hybrid deployments, inference costs, and lifecycle governance.
Teradata AgentBuilder is scheduled for private preview in Q4 2025, with general availability anticipated in early 2026. A special advance look at these new AI capabilities will be showcased at the company’s ‘Teradata Possible 2025’ event, taking place from October 6-8 in Los Angeles.
Also Read:
- SAS Unveils Retrieval Agent Manager to Enhance Generative and Agentic AI Capabilities for Enterprises
- Precisely Enhances Data Integrity Suite with New AI Agents and Context-Aware Copilot
Financially, Teradata, a $2.1 billion enterprise data analytics company with robust gross margins of 59%, appears well-positioned for this investment. Recent InvestingPro analysis suggests the company is undervalued, trading at an attractive PEG ratio of 0.24, and reported strong Q2 2025 performance with EPS of $0.47 and revenue of $408 million, demonstrating the financial strength to support its AI initiatives.
