Real-Time Context vs Real-Time Inference: Two Essential Patterns for Modern GenAI (and How DeltaStream Powers Both)

GenAI systems have advanced from basic chatbots to agentic systems capable of real-time interactions and processing live information, with DeltaStream serving as a pivotal streaming-native engine that enables both real-time context and inference at scale. These systems utilize two primary patterns: Real-Time Context for Agents, which equips AI agents with up-to-date information for decision-making and narrative reasoning, and Real-Time Inference Pipelines, which automate event-driven processes such as scoring and classification without human intervention. While Real-Time Context is suited for scenarios requiring explainable and consolidated state for agents, Real-Time Inference is optimal for high-throughput, automated decision-making tasks. Both patterns can coexist, as seen in use cases like fraud detection and customer support, where real-time scoring aids in decision-making while agents provide further analysis or explanation. DeltaStream integrates streaming SQL, real-time materialized views, and LLM/ML inference, making it a versatile platform for developing sophisticated AI systems that require real-time data processing and inference capabilities.

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