Teradata Corp. is expanding its Tera artificial intelligence assistant with a context engine and execution system designed to help agents carry out data tasks across enterprise systems.
Tera is an agentic AI workspace and natural-language interface the lets users interact with enterprise data and AI agents using natural language instead of writing SQL queries or code. The update aims to make Tera useful to business analysts, data engineers and database administrators through natural language requests.
Users can ask it to analyze information, build data pipelines or manage infrastructure while working within their organization’s access rules. Although Tera is part of the core Teradata Autonomous Knowledge Platform, the company said it can reach data held outside Teradata systems.
The new Tera Context Engine connects information from databases, catalogs, pipelines and other sources without requiring companies to move their data. It brings together metadata, data lineage, business definitions and access policies so an agent can interpret a request in the context of the organization using it. Teradata said the engine can trace AI outputs back to their sources and apply the same policies as information moves between systems.
A new feature called Tera Harness handles execution. It selects the tools, models and data needed for a task, tracks progress across multiple steps and can pause for human approval before taking sensitive actions. Teradata said the system can resume work after an infrastructure failure. It also applies controls before an action runs, an approach intended to limit unauthorized or destructive operations.
“Most enterprises are not starting from scratch with AI. They are dealing with tools that do not work together and a skills gap that makes those tools hard to use at scale,” Sumeet Arora, Teradata’s chief product officer, said in the announcement.
The third addition, Agent Skills, packages common data engineering, analysis and data science tasks into reusable functions. Teradata said agents can use the skills for work such as writing SQL or Python, optimizing queries, tuning workloads and sizing compute resources. Organizations can connect their own tools through the Model Context Protocol.
Teradata is also making a case for Tera’s operating costs. In company-reported testing on the SWE-bench Pro benchmark using the same Opus 5 model, Tera used 73% fewer tokens than Claude Code, completed tasks 42% faster and had 58% lower total cost, while achieving a higher task completion rate, the company said. On the data-eng-bench pipeline engineering benchmark, Teradata reported a 53% lower cost per reliably solved task than Snowflake Inc.’s Cortex Code, based on Snowflake’s published results. The company cautioned that comparisons reflect its testing and stated benchmark conditions, not performance across all enterprise workloads.
The additions come as businesses try to give AI agents enough information and authority to complete work without losing control of data access and approvals. Teradata said customers will be able to choose their models and run workloads in cloud, on-premises or sovereign environments.
It’s offering AI Services to help customers identify use cases and configure the business knowledge Tera needs for deployment. AI Services helps identify use cases for governed context, configure Industry Knowledge Models and move enterprise knowledge into production faster.
The company said the new capabilities, along with a set of specialized agent skills, will be available in the fourth quarter of 2026.
