Introducing Chalk MCP Server and Chalk Assistant: Run Agentic Machine Learning on Chalk

Chalk has introduced Chalk MCP Server and Chalk Assistant to integrate AI agents directly into machine learning development workflows, aiming to reduce the manual effort involved in gathering context, investigating models, engineering features, and validating changes. Using the Model Context Protocol, the MCP Server lets agents access Chalk capabilities such as reading feature definitions, running queries, analyzing data with Chalk SQL, diagnosing errors, engineering and backtesting features, and investigating model behavior. Chalk Assistant provides an in-product interface where users can connect an agent through their own model API key, ask it to examine models, features, or queries, and review its reasoning and proposed changes. The company positions these tools as a foundation for “agentic machine learning,” in which agents actively support continuous experimentation, faster iteration, and proactive model improvements within production ML systems.

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