Recent industry moves — Databricks acquiring Tecton to power real-time data for AI agents, Redis acquiring Featureform to deliver structured data into agents, and Hopsworks driving content explicitly on context engineering — signal a shift: feature stores are becoming context engines for GenAI and agentic systems.

Feast already has the core primitives (historical dataset creation, point-in-time correctness, online retrieval) to be a Feature Store and a Context Engine.

I propose that Feast 2.0 explicitly targets this role: an open-source context engine for AI agents.

Why This Matters

Instead of letting proprietary platforms own this space, Feast can be the vendor-neutral foundation for context engineering — powering both ML and agentic AI workloads.

Proposed Direction

• Add agent-oriented retrieval semantics (prompts, context windows, entity history, temporal snapshots).
• Strengthen low-latency real-time serving paths.
• Preserve existing ML feature-store workflows while broadening the abstraction toward “context”, not just “features.”
• Enhance the labeling mechanism so that features and context are more tightly coupled with labels

Request for Feedback

• Is broadening Feast’s mission toward “context engine” aligned with community needs?
• Which capabilities matter most for agentic workloads (latency, retrieval patterns, metadata/lineage, etc.)?

Some references:

• Databricks
• Redis
• Hopsworks
• Hopsworks Feature Store Summit