Teradata Transforms Tera into an Agentic Coworker
Teradata announced a major evolution of Tera, transforming it into an agentic coworker for enterprise data work. Where general-purpose AI assistants generate answers, Tera delivers outcomes. Through natural language and guided execution, everyone from business analysts to platform engineers and database administrators can analyze data, build AI applications, operate infrastructure, and automate complex workflows – all from a single governed environment that reaches enterprise data across platforms, not just within Teradata. Industry expertise and business knowledge are embedded across every interaction, secured by enterprise identity and policy, so no deep Teradata specialization is required.
Tera addresses two requirements enterprise AI has lacked: the ability to understand the business and the ability to act on that understanding. Teradata customers gain more reliable AI outcomes, better economics from every model interaction, and faster time to value, supported by Teradata AI Services for organizations that want to accelerate deployment.
Tera was purpose-built for enterprise data work, optimizing execution across analytics, vectors, and models. In testing on SWE-bench Pro using the same Opus 5 model, Tera consumed 73% fewer tokens than Claude Code while achieving higher task completion rates, completed work 42% faster, and incurred 58% lower total cost. On data-eng-bench, a benchmark developed by Snowflake Labs and Bespoke Labs for data pipeline engineering, Tera delivered 53% lower cost per reliably solved task than Snowflake Cortex Code using Opus 5, based on their published benchmark data. Across data-eng-bench and ADE-bench, Tera earned benchmark-leading accuracy, achieving the highest Pass3 score on data-eng-bench and tying for the top score on ADE-bench.
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