With new context engine, Teradata aims to be an AI colleague

Firming up Tera

Many enterprises are still struggling to move AI initiatives into production.

Reasons they're often failing in the pilot stage include insufficient data foundations and inadequate data and AI infrastructures. In addition, data management platforms that don't work well together and a dearth of workers with the skills to build and use AI tools contribute to the low success rate of AI projects.

The Tera transformation addresses the important enterprise challenge of fragmented tools and skills gaps while introducing vendor-neutral capabilities that work across existing data environments, not just Teradata systems.

The new Tera capabilities are designed to help Teradata customers overcome some of these barriers. In particular, they address platforms that aren't interoperable with its vendor-neutral Context Engine and the skills gap with Agent Skills.

"I'd describe the earlier Tera as a capable assistant. It could help you do things, but you were still doing most of the driving," Sumeet Arora, Teradata's chief product officer, told TechTarget. "[Now, it] can hold an objective, figure out what needs to happen next, coordinate across different tools and data sources, and carry the work through to a governed outcome without someone babysitting every step."

To serve agents with appropriate context from disparate data sources, the Tera Context Engine includes a context graph that connects business logic, neurosymbolic models that combine semantic mapping with Industry Knowledge Models and governance capabilities such as validation controls.

Tera Harness, which is built on a Go-native engine and the gRPC call framework, routes workflows by loading relevant memory and pre-planning work, monitoring progress and recovering from failures rather than abandoning tasks, and applying limits to unproductive iterations so AI tools don't get stuck in a failure loop that drives up costs.

Finally, Agent Skills includes a set of Platform Agents for managing the Teradata environment and a different set of Analytics Agents for working with data across platforms, both of which Tera automatically routes to the correct models and tools.

Like Catanzano, Devin Pratt, an analyst at IDC, called out the significance of enabling organizations to connect agents with context stored across data platforms.

"Enterprises already know they can prototype an agent," he told TechTarget, noting that his firm's research shows that fragmented, hard-to-access data is frequently cited as a reason organizations struggle to put agents into production. "What they are paying for is one that survives contact with production, and that means building it where the governed data already lives."

Numerous vendors provide the same individual layers Teradata is planning to deliver once the new Tera capabilities are GA, Pratt continued, specifically naming AWS, Databricks, Google Cloud, Microsoft and Snowflake. However, Teradata's open framework for AI is a distinguishing characteristic.

"The building blocks are everywhere now," he said. "What is rare is running governed agents directly on top of the mission-critical data that never leaves the building."

Missing links

Tera Context, Tera Harness and Agent Skills are logically designed to carry out Teradata's aim of turning Tera from a natural language assistant into a system of tools that can autonomously work alongside business users.

However, despite Teradata Harness including observability into how agents complete their work and capabilities that enable agents to recover from failures, Catanzano noted that what's missing is a feature that enables humans to correct and teach Tera.

"The three capabilities are well-architected for the agentic coworker vision, but there appears to be a gap in bidirectional learning mechanisms where users can naturally correct and teach Tera when outputs miss the mark or business context shifts," Catanzano said. "A more robust human-in-the-loop framework with contextual learning would strengthen the coworker positioning beyond task execution."

Pratt likewise noted that Teradata's three new Tera capabilities logically cover the AI lifecycle and further the vendor's aim of turning Tera into a coworker. Nevertheless, he says, Teradata could more fully deliver on its goal by adding testing, rollback when bugs or errors occur and versioning capabilities. In addition, more human involvement would be beneficial.

"The lifecycle is in the right shape," he said. "The next layer to build out is the control that lets an enterprise trust an agent with real responsibility."

Up next: more Agent Skills, more customer progress

As Teradata plots its next product development initiatives, helping customers move more of their AI pilots into production by providing AI services is a priority, according to Arora.

"A huge amount of enterprise AI investment is still sitting in experimentation," he said. "One of our biggest priorities is helping our customers graduate from 'we tried this and it was interesting,' to 'this is running in production and creating measurable value.' That means … helping people identify where governed context will make a difference to a business outcome and getting there faster."

In addition, Teradata is focused on adding more Agent Skills to aid organizations that don't have enough employees with the experience to build and use AI tools, Arora added.

"The agent skills we're shipping at GA are a starting point, not a ceiling," he said.

Pratt noted that the biggest step Teradata needs to take next is to prove that the new capabilities work as intended. That includes showing how named customers are using Tera to run critical AI workloads.

Catanzano, meanwhile, suggested that Teradata expand Tera by adding industry-specific agent skills that accelerate AI adoption and making Tera a means of orchestrating workflows that spread across different data and AI personas within organizations.

"Teradata should expand Tera into cross-functional workflow orchestration that facilitates handoffs between business analysts, data engineers and data scientists who speak different languages and work in different tools," he said.

Eric Avidon is a senior news writer for Informa TechTarget and a journalist with more than three decades of experience. He covers analytics and data management.