Reference architecture: Real-time data for agentic workflows

Real-time data systems require a precise architecture to ensure operational efficiency and safety, particularly in agentic workflows where decisions based on outdated information can lead to incorrect actions. These systems are prevalent in applications like fleet tracking, IoT monitoring, and multiplayer experiences, where timely updates are critical. The architecture must balance between streaming data and using batch updates to optimize costs and reduce risks, applying real-time data only where it significantly impacts decisions or safety. For instance, Climate LLC uses PubNub to facilitate real-time updates in its FieldView platform, allowing farm managers to monitor equipment effectively. The architecture involves defining "freshness bands" to determine the necessity of real-time data, ranging from sub-second updates for collision avoidance to hours-long intervals for model training. Key components include PubNub’s storage, playback, and presence features for tracking and coordination, alongside a disciplined approach to mapping data freshness to workflow steps and implementing dual-write systems for auditability. The use of verifiers before actions, short-lived tokens for access control, and deterministic replay are crucial for maintaining integrity and compliance. This architecture supports efficient decision-making, minimizes latency, and ensures that actions are based on the most current data, thereby enhancing reliability and performance in real-time systems.

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