Scale agents without scaling RAM costs

RAM prices have spiked, and agents are the workloads that feel it most: a single task fans out into dozens of steps, each one needing fast access to context, memory, and real-time signals. The default fixes (cap what you keep, move to disk, eat the bill) all mean compromise. Join us as we walk through where agent data gets expensive, and how to keep it fast at terabyte scale without an all-RAM bill.

• Context: KV caching to avoid recomputing shared context across agent steps, without the cache outgrowing your memory budget.
• Retrieval: Search and long-term memory over terabyte-scale data on a tiered index, not an all-RAM one.
• Decisions: Feature stores that give agents fresh signals for fraud detection, personalization, and ranking at the speed each one needs.
• Putting it together: One Redis engine with a RAM-to-SSD ratio you set per workload, so you pick the performance-cost mix instead of paying all-RAM prices.