Introducing Frame: Deterministic Data Operations for AI Agent Workflows

AI agents struggle to handle large enterprise datasets efficiently without losing context or introducing data transformation errors. Frame solves this by exposing deterministic columnar data operations—filtering, sorting, grouping, joining—as MCP tools callable directly by agents, eliminating hallucination risks and the need for external Python runtimes. This approach is critical for production workflows where agents must reliably manipulate tens of thousands of records without round-trips to external APIs.