Teradata has announced Enterprise AgentStack, a new enterprise-focused platform designed to help organizations move AI agents from experimental pilots into reliable, production-ready systems. The company positions the offering as a solution to one of the most persistent challenges in enterprise AI: deploying and managing multi-agent architectures without forcing developers into a single cloud provider or data ecosystem.

Enterprise AgentStack builds on Teradata’s earlier agent development tools and expands them into a more comprehensive framework. The platform combines three core components: Agent Builder, AgentEngine, and AgentOps. Together, these elements aim to cover the full lifecycle of AI agents, from design and orchestration to execution, monitoring, and governance.

Agent Builder provides a visual environment for creating agents and supports integration with popular third-party frameworks such as LangGraph, while also leveraging contextual intelligence capabilities. On top of that foundation, AgentEngine acts as the runtime layer, enabling agents to be deployed across hybrid and distributed infrastructures. AgentOps, meanwhile, delivers centralized visibility, offering tools for discovering agents, tracking performance, and managing updates and lifecycles across the organization.

Industry analysts note that the execution layer is a crucial addition. Without a standardized runtime, enterprises often resort to custom-built coordination logic, which can quickly become fragile and difficult to maintain. By formalizing how agents are executed and observed in real environments, Teradata aims to give organizations better insight into reliability, performance, and operational risk as deployments scale.

A different path from rival platforms

Analysts also point out that Teradata’s strategy contrasts with approaches taken by competitors like Snowflake and Databricks. Snowflake has been extending its Cortex services and Native App Framework to bring AI agents closer to governed data within its own platform. Databricks, on the other hand, has focused on agent workflows through Mosaic AI, tightly coupling model development, orchestration, and evaluation with its lakehouse architecture.

Teradata’s differentiation lies in its vendor-neutral positioning. Rather than anchoring AI agents to a single cloud or data stack, Enterprise AgentStack is designed as an execution and operations layer that can span hybrid environments. This flexibility is reinforced by Teradata’s reliance on external frameworks such as Karini.ai, Flowise, CrewAI, and LangGraph, allowing teams to evolve their agent architectures without being constrained by a single ecosystem.

Proof will come in production

While the architecture aligns well with enterprise requirements, analysts caution that real-world validation will be key. Organizations will want to see clear evidence that AgentStack can support complex, long-running, multi-agent workloads in production settings, not just controlled demos.

Ease of use will also be closely scrutinized. Enterprises need to understand how smoothly the platform handles policy enforcement, post-change evaluations, failure tracing, and integration with existing security and compliance systems. Openness and flexibility only deliver value if they do not push additional operational complexity back onto customers.

Teradata expects Enterprise AgentStack to enter private preview between April and June, with availability spanning both cloud-based and on-premises environments.