Streaming AI news from Next ‘26
Every device, user, and microservice generates data. Ingesting this data, extracting meaning and insights, and driving business decisions in real time has the potential to deliver transformational business value.The rise of agentic AI represents an opportunity for users to overcome the challenges inherent in real-time analytics. But while agentic AI has the potential to accelerate adoption, users face a new set of challenges with effectively leveraging real-time data:
- Real-time context is hard to implement. Teams will choose to incorporate data from batch-oriented approaches, like periodic database syncs and scheduled refreshes. Agents have to either rely on stale data or require memory-intensive context windows. This “context lag” makes them ineffective for real-time agentic tasks like fraud detection, dynamic e-commerce recommendations, or autonomous supply chain adjustments.
- Real-time systems are inflexible. Agentic tools lack the modularity to adapt to customer-specific requirements, forcing organizations to make difficult architectural choices. Data practitioners need a platform...
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