Kog

Kog identified that existing GPU inference setups were leaving substantial computational capacity unused during agentic AI workflows. The problem manifested most acutely for companies running large language model agents that required multiple reasoning steps—each step left GPUs idle while waiting for token generation to complete. This inefficiency was measurable: inference utilization rates often dropped below 30% during agent execution, directly translating to wasted infrastructure costs. Teams had attempted workarounds like batching requests, using smaller models, or accepting latency penalties, but none addressed the fundamental mismatch between GPU architecture and agent behavior patterns. Early validation came from conversations with inference infrastructure providers and AI companies operating at scale. These users immediately recognized the problem when Kog demonstrated how their current setups were bottlenecked. The fact that multiple infrastructure teams independently confirmed they'd observed but hadn't solved this issue suggested a genuine market gap. Additionally, the technical feasibility of their approach—rethinking how computations were scheduled during agent execution—resonated with GPU manufacturers exploring optimization opportunities.