AI developers waste tokens and incur excessive costs through redundant tool calling and context processing

AI/LLM application developers struggle with ballooning API costs and slow inference times because language models repeatedly call the same tools and reprocess identical context windows. Current solutions force developers to choose between expensive token usage or building complex custom caching systems, leaving them unable to efficiently scale AI applications without significant engineering overhead or budget overruns.