Powering AI Agents in Production with a Real-Time Context Engine

AI agents are moving from answering questions to taking action inside live systems. But most production initiatives stall for one reason: agents operate on stale or incomplete context.

Batch pipelines, delayed updates, fragmented event streams, and runtime glue code were built for analytics, not autonomous decision-making. As agents begin triggering workflows, updating records, approving transactions, or responding to threats, context freshness becomes the difference between correctness and failure.

In this session, Hojjat introduces DeltaStream’s Real-Time Context Engine for AI Agents and explains why continuously updated, inference-ready operational state is the missing infrastructure layer in modern AI architectures.

Security will be explored as one high-impact example, but the architectural principles apply broadly across fintech, SaaS, security, marketplaces, and any event-driven system where correctness matters in real time.

What You’ll Learn

• Why AI agents fail in production even when models are strong
• Why context freshness is now a first-class system requirement
• How to eliminate brittle streaming glue code and runtime queries
• How to unify streaming, real-time, and batch workflows
• How to move AI agents from pilot to production safely