Treating AI Agents as Distributed Systems: The Hidden Performance Challenge

High-performance AI agents share the same optimization challenges as distributed systems: managing massive context while reducing latency. The critical insight is that model speed alone doesn't determine responsiveness; instead, how you architect input token processing (prefill phase) and output token generation (decode phase) matters most. This demands thinking about token math and distributed systems patterns like scatter-gather and pipelining, not just raw model throughput.