A Million Agents Is a Distributed System Problem

When deploying multiple AI agents, the challenge becomes a distributed systems problem rather than an intelligence problem—agents need to be scheduled and managed like processes, with their state persisted rather than keeping everything in context. Research shows that multi-agent coordination can improve parallel work efficiency but requires proper orchestration through kernels and schedulers to avoid error amplification and resource waste. Treating agents as processes with interruption, resumption, and memory management capabilities—rather than systems that run indefinitely—improves performance by up to 2× while reducing token usage and latency.