Agentic AI for the enterprise: Powered by Teradata Enterprise AgentStack

Agentic AI for the enterprise: Powered by Teradata Enterprise AgentStack

Build, deploy, and govern enterprise‑grade AI agents quickly, safely, and at scale.

Barriers to agentic AI

Critical gaps that undermine agentic AI success

Most AI initiatives fail because they never move beyond experimentation. The main obstacle is managing the complex agent lifecycle across build, integration, deployment, and governance.

  • Injecting trusted data securely.
  • Embedding context, logic, and policies.
  • Prototype → production tooling gaps.
  • Enforcing permissions, guardrails, isolation. 
  • Packaging agents, tools, models, and memory.
  • Agents don’t sleep and will ask questions. Need a high-scalability, cost-effective, reliable platform.
  • Connecting multiple data sources (hybrid).
  • Ensuring consistency, security, and compliance.
  • Visibility into reasoning, tools, and actions. 
  • Governance, compliance, and security. 
  • Managing drift, degradation, cost, and explainability. 
AgentStack capabilities

Powering agentic AI with enterprise-grade intelligence

Teradata Enterprise AgentStack unifies everything needed to build, deploy, and govern AI agents. AgentBuilder accelerates creation using no‑code and pro‑code tools, while the Enterprise MCP provides secure, context‑rich access to trusted enterprise data.

AgentEngine delivers scalable execution across hybrid environments, and AgentOps centralizes monitoring, governance, and guardrails. Together, they help organizations move from AI pilots to production‑grade autonomous agents quickly and safely.

Enterprise AgentStack

AgentBuilder: Build intelligent agents faster using open-source no-code and pro-code tools integrated with Teradata Vantage.  

Teradata Enterprise MCP is a curated set of tools, prompts, and resources designed to simplify and enhance interaction with Teradata Vantage.

AgentEngine: Provides a seamless way to run agents by providing memory ensuring consistency, reliability, and enterprise readiness.

AgentOps: Capabilities to support the full lifecycle of intelligent agents—from rapid prototyping to enterprise-grade deployment and governance. included built-in features for compliance, auditability, and policy enforcement, ensuring that agents operate within defined guardrails and governance frameworks.

Use cases

How agentic AI drives measurable outcomes across the enterprise

We enable expert agents enriched with industry context and enterprise-wide intelligence to drive more autonomous, informed decision-making—transforming workflows, accelerating decisions, and unlocking new business value across marketing, sales, service, and risk management.

Frequently asked questions

Teradata AgentStack FAQs

AgentBuilder FAQ

Teradata AgentBuilder is a set of capabilities that enables enterprises to build and manage autonomous AI agents with contextual knowledge, domain expertise, and hybrid deployment flexibility.

  • Analyst validation: Recognized by Gartner and Forrester as top growth area.
  • Market demand: Enterprises struggle to get value from AI agents.
  • Strategic alignment: It’s a catalyst for Teradata’s future.

Despite growing interest in agentic AI, enterprise decision-makers, data scientists, AI practitioners, and IT leaders continue to face significant barriers to operationalizing autonomous agents. These include fragmented and siloed data that 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.

We enable expert agents enriched with industry context and enterprise-wide intelligence to drive more autonomous, informed decision-making.

  • Build and Manage Autonomous AI Agents: AgentBuilder enables enterprises to create and orchestrate autonomous AI agents that leverage trusted data, deep domain expertise, and hybrid deployment flexibility (cloud and on-premises).
  • Low/No-Code Agent Creation: Provides a user-friendly interface (UI) for building agents and developing multi-agent workflows. Supports open-source tools like Flowise, CrewAI, and LangGraph, as well as integration with partner and cloud service provider tools.
  • Pre-Built Expert Agents: Includes a suite of pre-built agents for rapid adoption and immediate business impact, such as:
    • SQL Agent: Converts natural language into optimized SQL queries for Teradata data warehouses.
    • Data Science Agent: Builds fully executable machine learning pipelines from natural language requests.
    • Monitoring Agent: Continuously monitors and manages Teradata databases and infrastructure for health and performance.
  • Custom Agent Development: Users can build their own agents and use cases, leveraging Teradata’s Model Context Protocol (MCP) Server for secure, scalable agent deployment. The MCP Server provides connectivity, authorization, and extensibility for additional tools and agent studios (e.g., Bedrock, Azure, GCP).
  • Enterprise-Grade Governance: Built-in features for compliance, auditability, and policy enforcement, ensuring responsible and secure deployment of autonomous agents.
  • Embedded Business Knowledge: Agents can be enriched with industry context, enterprise-wide intelligence, and reusable assets (like industry data models), enabling more relevant, domain-aware, and business-aligned recommendations.
  • Hybrid and Scalable Deployment: Supports deployment across cloud and on-premises environments, with tools for efficient, autonomous data processing and decision-making at scale.
  • Open and Extensible Ecosystem: AgentBuilder is compatible with open-source frameworks and allows users to contribute tools back to the product team, fostering continuous improvement and innovation.

Yes, we are providing the following expert agents:

  • Customer Lifetime Value agent is an expert agent purpose-built for CLV use cases, empowering data exploration, actionable insights, and strategic decision-making.
  • Teradata Data science agent is an expert agent that creates fully executable ML pipelines from natural language requests.
  • Teradata SQL generation agent is an advanced expert agent that converts questions or requests expressed in natural language into SQL queries that can be executed against Teradata data warehouse tables.
  • System health monitoring agent automates overseeing of Teradata databases, servers and sub-systems to maintain reliability, detect issues and optimize performance.

Customers can leverage an AgentBuilder to autonomously solve business problems. These agents will operate within the structure of the industry framework such as Customer Intelligence Framework and be tightly coupled with our industry data models.
Intelligence Framework

  • Trusted, Contextual data at scale: Teradata Vantage unifies structured and unstructured data across cloud and on-prem environments, ensuring AI agents make accurate, real-time, context-aware decisions.
  • Embedded business knowledge and deep domain expertise: Agents can use Teradata’s industry expertise and reusable assets to create prompts and workflows that deliver domain-aware, business-aligned recommendations.
  • Scalable, efficient tools for autonomous agents in hybrid environment: With 150+ in-engine functions, BYOM/BYO-LLM support, and RAG capabilities, Teradata enables agents to process and act autonomously on data directly within the platform—minimizing latency and cost.
  • Enterprise-grade governance: Built-in features for compliance, auditability, and policy enforcement, allowing to deploy autonomous agents responsibly and securely.
  • Pre-built agents and expert agents: Pre-built Agents offer ready-to-use templates for tasks like churn prediction and system monitoring designed to help teams get started quickly with agentic AI.

Currently, the AgentBuilder installation is separate from Vantage.    

AgentBuilder requires two installations: a front-end (currently Flowise) and Teradata MCP server, which can connect to VantageCloud Enterprise or VantageCloud Lake.

There is no specific restriction related to version, but it is recommended to be 17.20 onwards to leverage MCP tools related to advanced functionality such as vector store.

Currently, both Flowise and the Teradata MCP server are open-source, so there’s no associated cost. However, new features coming in 2026 will include additional costs.

Customers can join the waitlist for private preview. In the meantime, customers can contact the accounts team for a demo.

AgentStack FAQ

AgentStack is a Teradata product that enables customers to move from isolated pilots to production-grade agents by simplifying the entire agent lifecycle.

Most organizations remain stuck navigating the transition from agent experimentation to scaled deployment. The key challenge in realizing tangible value from AI agents is managing the complexity of the AI agent lifecycle.

  • AgentBuilder: Enables building with no-code and pro-code tools
  • Enterprise MCP: Curated set of tools, prompts, and resources designed to simplify and enhance interaction with Teradata Vantage® 
  • AgentEngine: A seamless way to run agents by providing memory and ensuring consistency, reliability, and enterprise readiness 
  • AgentOps: Provides capabilities to support the full lifecycle of intelligent agents

Teradata simplifies building, deploying, and monitoring enterprise agents by offering an integrated stack of key capabilities:  

  • Quickly build production-ready agents which leverage knowledge, contextual intelligence, and applying reasoning to deliver autonomous outcomes
  • Enterprise-grade integration to facilitate interactions between agents and enterprise data and leverage scalable analytics
  • Easily deploy, monitor, and manage agents with security and governance at scale across a hybrid landscape
  • Highly scalable and cost-effective deployment of agents leveraging the Teradata AI + Knowledge Platform

Currently, the AgentStack installation is separate from Vantage.

There are no strict version restrictions. However, it’s recommended to use version 17.20 or later to take advantage of advanced MCP tools like vector store functionality.

Cloud deployments use Kubernetes, while on-premises setups are available through TMS. MCP will support RPM, Docker, and Kubernetes.

Related

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