Enterprise AI agents should optimize memory, context, and cache together
Compound should optimize memory, context, and cache-aware state design as one system instead of treating them as separate parts added later. These agents often have a clear and limited set of actions they are allowed to take.
That narrow action space can be used to make more opinionated design choices instead of building a fully general setup. The details are based on DSPy and GEPA.
Key points
- The focus is on the runtime structure of .
- Memory, context, and cache-aware state design should be tuned together.
- agents often have limited action spaces, which can make simpler and more targeted designs possible.
- The approach is based on DSPy and GEPA.
- The cost angle depends on reducing repeated work and unnecessary context sent to the model.