Cloud-native data processing is evolving from static pipelines to autonomous "Data Agents" powered by in-flight LLMs. However, integrating high-throughput event streaming (Kafka) with high-latency AI compute creates massive distributed systems friction. Traditional stream processors choke on LLM latency, causing unmanageable consumer lag and cascading failures. This talk explores a novel pattern to solve this impedance mismatch: decoupling throughput from reasoning via event-driven serverless architectures (Knative). We demonstrate building "Sense and Act" data agents that leverage container concurrency to natively absorb AI processing variance. We also introduce "Localized Reflexes"—embedding in-process query engines (chDB) directly into serverless pods. This creates self-contained edge nodes that bypass expensive LLM network hops entirely. Finally, we dissect stateless, stateful, and edge-agent topologies to operationalize reliable agentic workflows on Kubernetes.

The AI Infrastructure Meetup: BLR. vCluster+Cloudera