SAN DIEGO – Sept. 23, 2025 – Teradata (NYSE: TDC) today announced the launch of AgentBuilder, a suite of capabilities designed to accelerate development and deployment of autonomous, contextually intelligent AI agents.
Leveraging open-source frameworks and powered by the Teradata AI and knowledge platform, AgentBuilder gives teams the ability to quickly design, operationalize, and manage multi-agent systems that draw on Teradata Vantage’s data, advanced analytics and hybrid infrastructure. The new capabilities also include ready-to-deploy Teradata Agents—task-driven templates that accelerate implementation and deliver rapid impact for complex, domain-specific challenges.
Despite growing interest in agentic AI, there are significant barriers to operationalizing autonomous agents. These include fragmented and siloed data that may lead to hallucinations and unreliable outputs, a lack of embedded business knowledge that limits contextual relevance, performance bottlenecks and inflated costs from prompt-driven workloads, and insufficient governance that can make deployment risky.
To address these challenges, Teradata’s AgentBuilder offers capabilities for developing and deploying autonomous AI agents. By integrating contextual knowledge, deep domain expertise, and scalable hybrid infrastructure, AgentBuilder empowers organizations to build and manage multiple agents that can operate securely and autonomously across cloud and on-premises environments. For example, Teradata’s new Teradata Agents are specifically designed with domain expertise and task-specific logic, entrenching business context directly into the agent’s behavior to produce more relevant and trustworthy outputs. Powered by Teradata’s comprehensive knowledge platform, AI, and analytics capabilities—including its recently launched Model Context Protocol (MCP) Server—AgentBuilder enables teams to transition from agentic AI experimentation to production, unlocking meaningful business value at scale.
“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.”
“Add to that our seamless support across cloud and on-premises environments, and we’re delivering a level of flexibility, integration, and contextual intelligence that sets Teradata apart. This is not just about data—it’s about delivering trusted, transparent, and complete knowledge to power the next generation of AI,” added Arora.
Building on this foundation, the Teradata MCP Server provides the core infrastructure that powers AgentBuilder’s intelligent agent capabilities. MCP equips developers and AI practitioners with a curated set of prompts, components, and resources that streamline access to the Teradata Vantage platform—enabling agents to query, reason, and act with greater precision. By integrating MCP into AgentBuilder, Teradata ensures that agents are not only context-aware and knowledgeable, but also secure, scalable, and aligned with enterprise standards. This tight coupling accelerates development, enhances reliability, and enables organizations to unlock the full potential of agentic AI across their hybrid environments.
In this early release, AgentBuilder will support popular open-source frameworks such as Flowise and CrewAI, with LangChain and LangGraph planned to follow. These tools provide flexible, modular building blocks for constructing agent workflows, memory, reasoning, and coordination—essential components for developing autonomous systems. Combined with Teradata’s enterprise-grade scalability, governance, and performance, this approach accelerates the path to real-world, agentic AI applications that continuously learn and evolve their knowledge base.
Teradata AgentBuilder also includes Teradata Agents—pre-built templates for tasks such as churn analysis and system monitoring—designed to help teams quickly adopt agentic AI. These agents address high-value, domain-specific challenges by applying advanced analytics, behavioral data, and contextual awareness to complex operational processes. They deliver explainable, business-aligned outcomes and operate autonomously across business functions. Continuously learning and adaptive, Teradata Agents are ideal for high-impact use cases that demand precision, scalability, and trust.
Examples of Teradata Agents include:
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Teradata SQL agent is an advanced technical agent that converts questions or requests expressed in natural language into SQL queries that can be executed against Teradata data warehouse tables. It can discover schemas and table definitions, and detect, correct, and optimize query statements. This agent can be used with open-source frameworks in a multi-agent system.
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Teradata data science agent is a technical agent offered by Teradata that creates fully executable ML (machine learning) pipelines from natural language requests. It combines the power of LLMs, Teradata MCP tools (part of the MCP Server), and linguistics which helps with multi-step reasoning, contextual understanding, and action execution across the ML workflow steps to generate reports and insights.
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Teradata monitoring agent is an intelligent automation tool designed to continuously monitor and manage Teradata databases, servers, and sub-systems. By leveraging real-time telemetry and system insights, the agent proactively maintains system health, detects anomalies before they escalate, and optimizes performance across the enterprise.
Teradata AgentBuilder will be available in private preview in Q4 2025.
For a special sneak peek of AgentBuilder, including opportunities to be hands-on with it, attend Teradata Possible 2025, Oct. 6-8 in Los Angeles.
