Infrastructure for Multi-Agent Systems

AI Agent Evolution - AI agents are evolving into distributed workflows with many sub-agent calls in a single run [1] - These multi-agent systems are useful for long-running workflows and agentic map reduce jobs [1] - These systems apply human-level judgment to filter and search through large amounts of data in parallel [1] Challenges in Building Multi-Agent Systems - Building these systems requires solving traditional distributed systems problems to ensure high throughput and reliability while controlling costs [2] - New problems include writing effective agent and sub-agent prompts, handling untrusted context, and monitoring and debugging agents [2] Call to Action - The industry is looking for builders who have felt this pain in production and want to create tools to make these systems easier to build and maintain [2] - The industry aims to make operating fleets of agents as routine and reliable as deploying a web service or running a Spark job [3]