Enterprise AI agents today face a fundamental timing problem: They can’t easily act on critical business events because they aren’t always aware of them in real-time.The challenge is infrastructure. Most enterprise data lives in databases fed by extract-transform-load (ETL) jobs that run hourly or daily — ultimately too slow for agents that must respond in real time.One potential way to tackle that challenge is to have agents directly interface with streaming data systems. Among the primary approaches in use today are the open source Apache Kafka and Apache Flink technologies. There are multiple commercial implementations based on those technologies, too, Confluent, which is led by the original creators behind Kafka, being one of them.Today, Confluent is introducing a real-time context engine designed to solve this latency problem. The technology builds on Apache Kafka, the distributed event streaming platform that captures data as events occur, and open-source Apache Flink, the stream processing engine that transforms those events in real time.The company is also releasing an open-source framework, Flink Agents, developed in collaboration with Alibaba Cloud, LinkedIn and Ververica. The framework brings event-driven AI agent capabilities directly to Apache Flink, allowing organizations to build agents that monitor data streams and trigger automatically based on conditions without committing to Confluent’s managed platform.”Today, most enterprise AI systems can’t respond automatically to important events in a business without someone prompting them first,” Sean Falconer, Confluent’s head of AI, told VentureBeat. “This leads to lost revenue, unhappy customers or added risk when a payment fails or a network malfunctions.”The significance extends beyond Confluent’s specific products. The industry is recognizing that AI agents require different data infrastructure than traditional applications. Agents don’t just retrieve information when asked. They need to observe continuous streams of business events and act automatically when co …
