Proposal: Evolve Feast into a Context Engine for AI Agents (Post 1.0)

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](https://www.databricks.com/blog/tecton-joining-databricks-power-real-time-data-personalized-ai-agents)
- [Redis](https://www.globenewswire.com/news-release/2025/10/09/3164211/0/en/Redis-Acquires-Featureform-to-Help-Developers-Deliver-Real-time-Structured-Data-into-AI-Agents.html
)
- [Hopsworks](https://www.youtube.com/watch?v=GM5z6-NaToE)
- [Hopsworks Feature Store Summit](https://www.featurestore.org/feature-store-summit-2025)