Simplifying the building, deployment, and management of enterprise AI agents
The rise of the autonomous enterprise
The enterprise landscape is undergoing a fundamental transformation. Organizations that can leverage their knowledge, apply agentic reasoning, and deploy intelligent systems are positioned to deliver better, faster, and more cost-effective outcomes for their customers. This shift toward the autonomous enterprise isn't just a buzzword—it's becoming a business imperative.
Technology stack for autonomous enterprise
The technology stack powering this transformation looks radically different from traditional SaaS architectures. A new enterprise stack is emerging, built on three critical layers: a unified knowledge layer that integrates data, context, and insights; an intelligent agent layer where AI systems reason, decide, and act; and a measurable outcomes layer that optimizes processes and delivers tangible business results.
The AI agent paradox: Massive potential and persistent challenges
According to BCG research,1 organizations implementing AI agents effectively can achieve up to 5x ROI. Yet the stark reality remains: 95% of AI initiatives fail to deliver their promised value. This disconnect isn't about the technology itself—it's about what makes AI autonomy possible at enterprise scale.
Most organizations remain stuck navigating the difficult transition from agent experimentation to scaled deployment. Gartner² predicts that over 40% of agentic AI projects will be canceled by 2027 due to rising costs, unclear business value, and inadequate risk controls. The key challenge? Managing the complexity of the AI agent lifecycle.
Moving from prototype to production: Key challenges
Enterprise teams face obstacles at every stage of the agent lifecycle:
- Build challenges: Injecting trusted data securely, embedding context, logic, and policies, and bridging the gap between prototype and production tooling.
- Integration challenges: Connecting multiple data sources across hybrid environments while ensuring consistency, security, and compliance.
- Deployment challenges: Enforcing permissions, establishing guardrails and isolation, packaging agents with their associated tools, models, and memory, and delivering scalable, cost-effective, and dependable platforms—since agents operate around the clock and continuously handle incoming requests.
- Operational challenges: Maintaining visibility into reasoning, tools, and actions; ensuring governance, compliance, and security; and managing drift, degradation, cost, and explainability.
Introducing Teradata Enterprise AgentStack
Teradata Enterprise AgentStack empowers organizations to move from isolated pilots to production-grade agents by simplifying the entire agent lifecycle. It provides the capabilities for building, deploying, and governing autonomous AI agents on a unified AI + Knowledge Platform—enabling faster, safer innovation at scale with confidence.
The AgentStack delivers end-to-end capabilities across three core areas:
AgentBuilder: Accelerate agent development
AgentBuilder enables enterprises to build autonomous AI agents with trusted data, domain expertise, and hybrid deployment flexibility. It offers an open-and-connected approach, supporting both no-code and pro-code development tools.
Key features include a UI interface for building agents and developing multi-agent workflows using open-source tools like Flowise, LangGraph, and CrewAI, as well as partner tools from cloud service providers and NVIDIA. Context intelligence combines Teradata IP—including industry data models, frameworks, and expert prompts—with open-and-connected LLM APIs.
The platform also includes expert agents developed by Teradata, such as the Business Agent for Customer Lifetime Value, SQL Agent, Data Science Agent, and Monitoring Agent.
Enterprise MCP: Secure, context-rich interactions
The Enterprise MCP (Model Context Protocol) enables quick, secure, and context-aware communication between AI models or agents and the Teradata Vantage® platform, which offers data, analytics, and AI capabilities. It fully supports Teradata's resources and services by allowing tools discovery, standardizing APIs for smooth collaboration between models and agents, and managing context to maintain memory and continuity throughout interactions.
Enterprise-grade security and control enforce policies for authorized access to business data and functions, while built-in audit and logging capabilities support compliance, transparency, and tracing. The architecture is designed for high-throughput, low-latency, and fault-tolerant operations.
AgentEngine: Enterprise-grade execution
AgentEngine provides scalable and enterprise-grade execution of AI agents. It supports single agents, multi-agent systems, and custom workflows across any agentic framework. The polyglot runtime allows agents built with multiple frameworks to run together seamlessly, sharing memory, data structures, and execution workflows.
The platform enables AI agents to seamlessly and securely access LLMs, MCP tools, and integrations, with hybrid deployment options across Kubernetes, Docker, and TMS (Teradata Managed Server). A dedicated memory and context layer enables both short-term and long-term memory, leveraging structured and unstructured data.
AgentOps: Unified management and governance
AgentOps provides a unified UI delivering centralized discovery, monitoring, and lifecycle management for all AI agents. It ensures enterprise-grade security and governance through robust policy enforcement, with built-in guardrails, evaluations, compliance checks, and human-in-the-loop controls keeping agents safe, predictable, and aligned with business goals.
An integrated agent playground enables rapid prototyping and experimentation, allowing teams to move from idea to production with confidence—accelerating innovation without sacrificing control. The platform also includes an agent evaluation framework that assesses the quality of queries and results for given prompts.
Why Teradata Enterprise AgentStack?
Teradata simplifies building, deploying, and monitoring enterprise agents by offering an integrated stack of key capabilities that enable the autonomous enterprise:
- Rapid development: Quickly build production-ready agents that leverage knowledge and contextual intelligence, apply reasoning, and deliver autonomous outcomes
- Enterprise-grade integration: Facilitate interactions between agents and enterprise data while leveraging scalable analytics
- Comprehensive governance: Easily deploy, monitor, and manage agents with security and governance at scale across hybrid landscapes
- Cost-effective scale: Highly scalable and cost-effective deployment of agents leveraging the Teradata AI + Knowledge platform
Get started
Ready to explore how agentic AI can transform your enterprise? Here's how to take the next step:
- Schedule an Executive Briefing Center session to discuss agentic AI use cases specific to your industry
- Contact your Teradata sales team to learn more about Enterprise AgentStack and begin your journey toward the autonomous enterprise
Sources
1. Jessica Apotheker et al., “The Widening AI Value Gap,” BCG, 2025, https://www.bcg.com/publications/2025/are-you-generating-value-from-ai-the-widening-gap.
2. Cameron Haight, “AI Agents Will Transform Enterprise IT Operations,” Gartner, 2025, https://www.gartner.com/en/documents/6224087.