AI agents often re-derive the same workflows, wasting time and resources. A service that stores and retrieves these workflows would save compute time and latency.
Build a cloud-based cache system that integrates with major AI platforms, storing workflow outcomes for reuse. AI developers would pay for reduced latency and lower compute costs.
Start with a simple key-value store for common workflows, then expand to complex dependencies. The biggest risk is ensuring cache coherence and correctness across agent versions.