Agents need timely and relevant context data to work effectively in an interactive environment. If an agent takes more than a few seconds to react to an action in a client applicatoin, users will not perceive it as intelligent - just laggy.

Real-time context engineering involves building real-time data pipelines to pre-process application data and serve relevant and timely context to agents. This talk will focus on how you can leverage application identifiers (user ID, session ID, article ID, order ID, etc) to identify which real-time context data to provide to agents. We will contrast this approach with the more traditional RAG approach of using vector indexes to retrieve chunks of relevent text using the user query. Our approach will necessitate the introduction of the Agent-to-Agent protocol, an emerging standard for defining APIs for agents.

We will also demonstrate how we provide real-time context data from applications inside Python agents using the Hopsworks feature store. We will walk through an example of an interactive application (TikTok clone).

The Topics covered include :

• Context Engineering for Agents
• Real time context retrieval
• Similarities between feature engineering and context engineering (extracting and compressing data)
• Shift-left streaming data pipelines vs shift-right on-demand context computation

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