Teradata has expanded the agent-building capabilities it launched last year into a full-blown toolkit, which it says will help enterprises address the challenge of moving AI agents beyond pilots into production-grade deployments.

Branded as Enterprise AgentStack, the expanded toolkit layers AgentEngine and AgentOps onto Teradata’s existing Agent Builder, which includes a user interface for building agents with the help of third-party frameworks such as LangGraph, and a context intelligence capability.

While AgentEngine is an execution environment for deploying agents across hybrid infrastructures, AgentOps is a unified interface for centralized discovery, monitoring, and lifecycle management of agents across a given enterprise.

The AgentEngine is a critical piece of Enterprise AgentStack as it sits between agent design and real-world operations, saidHyperFRAME Research’s practice leader of AI stack Stephanie Walter.

“Without an execution engine, enterprises often rely on custom glue code to coordinate agents. The Agent Engine standardizes execution behavior and gives enterprises a way to understand agent performance, reliability, and risk at scale,” Walter said, adding that AgentEngine-like capabilities are what enterprises need for moving agents or agentic systems into production.

However, analysts say Teradata’s approach to enterprise agent adoption differs markedly from that of rivals such as Databricks and Snowflake.

While Snowflake has been leaning on its Cortex and Native App Framework to let enterprises build AI-powered applications and agents closer to governed data, Databricks has been focusing on agent workflows through Mosaic AI, emphasizing model development, orchestration, and evaluation tied to its lakehouse architecture, Robert Kramer, principal analyst at Moor Insights and Strategy, said.

Seconding Kramer, Walter pointed out that Teradata’s differentiation lies in positioning Enterprise AgentStack as a vendor-agnostic execution and operations layer designed to work across hybrid environments, rather than anchoring agents tightly to a single cloud or data platform.

That positioning can be attributed to Teradata’s reliance on third-party frameworks such as Karini.ai, Flowise, CrewAI, and LangGraph, which give enterprises and their developers flexibility to evolve their agent architectures over time without being locked onto platforms from Snowflake and Databricks that tend to optimize for end-to-end control within their own environments, Walter added.

However, the analyst cautioned that, although Enterprise AgentStack’s architecture aligns well with enterprise needs, its litmus test will be to continue maintaining deep integrations with third-party frameworks.

“Customers will want to see concrete evidence of AgentStack supporting complex, long-running, multi-agent deployments in production,” Walter said.

Kramer, too, pointed out that enterprises and developers should try to understand the depth of usability before implementation.

“They need to check how easy it is to apply policies consistently, run evaluations after changes, trace failures end-to-end, and integrate with existing security and compliance tools. Openness only works if it doesn’t shift complexity back onto the customer,” Kramer said.

Enterprise AgentStack is expected to be made available in private preview on the cloud and on-prem between April and June this year.