AI agents consume prohibitive token costs and lose critical context at scale

Developers building AI agents face exploding inference costs and degraded performance as context windows grow, forcing them to choose between expensive comprehensive memory or cheap but forgetful systems. Current architectures lack efficient memory management strategies, causing agents to either hemorrhage money on redundant token processing or fail at complex multi-step tasks requiring historical context. This architectural gap makes production AI agents economically unviable for most use cases.