Workload-Aware Caching for Multi-Agent Systems

Multi-agent systems utilize directed acyclic graphs for task decomposition, enabling caching of intermediate results. A new workload-aware eviction policy optimizes memory by scoring recomputation cost, DAG dependency count, and agent invocation frequency. Separately, caching in SaaS environments prevents database overload during traffic spikes. By serving slightly outdated data, caching transforms potential slow requests into manageable failures, ensuring system survival during production surges, which is a core focus of the Full Stack SaaS Masterclass.