MapReduce for AI Agents: Scaling Multi-Agent Workloads on the JVM

Not every agent workload is a pipeline. Sometimes you have a list of items -- documents to summarize, products to review, regions to analyze -- and you need the same kind of analysis applied to each one, then a synthesis of all the results. That's MapReduce. And it turns out to be one of the most practical patterns for production agent systems. Most agent frameworks don't have a first-class abstraction for this. You end up writing a loop, creating agents and tasks dynamically, wiring up dependencies by hand, and hoping you got the concurrency right.