Anthropic Inks Deal to Bring Its AI Model to Databricks Platform

Artificial intelligence (AI) company Anthropic has launched a five-year partnership with data/artificial intelligence (AI) firm Databricks.

The collaboration, announced Wednesday (March 26), will bring Anthropic’s AI models and services to the Databricks Data Intelligence Platform, offering Anthropic’s Claude model to more than 1,000 companies.

According to a news release from Databricks, this will allow customers to build and deploy AI agents that reason over their own data with Anthropic’s latest frontier model, Claude 3.7 Sonnet.

“Enterprises want to increase returns on their AI investments,” the release said. “Yet, many struggle to build, deploy and evaluate AI agents that can reason over their enterprise data and meet production-level requirements for accuracy, security and access control.”

The companies say they help solve this challenge by combining Databricks Mosaic AI — which can build “domain-specific AI agents on organizations’ unique data” — with Claude models, which can “optimize for real-world tasks that customers find most useful.”

The partnership comes a little more than a week after comments from an Anthropic executive that the startup was focusing its attention on enterprise customers.

Speaking to the Financial Times, Mike Krieger, Anthropic’s chief product officer, said the company was concentrating on how its technology could “help with knowledge work” for “people who spend all day in meetings or in Excel or Google Docs. It feels like we should be able to make that easier.”

Meanwhile, PYMNTS explored the concept of agentic AI in a recent interview with George Westerman, senior lecturer at the MIT Sloan School of Management.

He said that while there’s no agreed-upon definition for agentic AI, it is at its simplest software code that will process information and make a decision or carry out an action.

“The concept is not new,” he added, noting that many of Microsoft Windows’ processes routinely launch and relaunch. And Wall Street has used automated trading for decades.

But while the actions on rules-based bots are pre-determined, AI agents built on top of generative AI models can dynamically deliver responses, understand, adapt and learn while having autonomy and decision-making abilities to complete assigned tasks.

“It can do things that traditional computing could not do: It can hold a conversation with a client. It can process unstructured data and documents,” Westerman said. However, “we still want to use the same processes to evaluate how to do it.”