Agentic Plan Caching (APC), described in the paper published by Stanford researchers on January 26, 2026, lets AI agents reuse structured plan templates from prior executions instead of re-invoking expensive LLMs for every new task. It achieved 76% cost reduction on benchmarks.

Using different sources we create projections for growth using a simple growth model:

Plans/year = ActiveAgents x PlansPerDay x 365, and Storage = Plans x BytesPerPlan

MarketsandMarkets forecasts the AI agent market growing from $7.8B to $52.6B by 2030 at 46% CAGR. IDC projects 1.3 billion deployed AI agents by 2028. Gartner says 33% of enterprise software will be agentic by 2028, up from under 1% in 2024, with 15% of daily work decisions made autonomously. These three forecasts together imply that agent-driven plan generation will scale explosively — at just 5-20 plans per agent per day, 1.3 billion agents means 2.4 to 9.5 trillion plans

per year across the ecosystem by 2028.

We evaluate a possible storage offloading tipping point using all this data. Raw plan text is cheap at 2-10 KB each, but production systems also store retrieval embeddings, keyword indexes, tool call traces, and trajectory logs — inflating effective bytes per plan by 10-100x. That is the silent killer. Under conservative assumptions (1M agents, 30% YoY growth, lean plans), everything fits in RAM for years. Under aggressive assumptions (100M agents, 80% YoY, rich metadata), SSD offload becomes structurally

inevitable in year one — you simply cannot fit petabytes of cached plans in RAM.

The paradox is that APC's own success makes hoarding worse: every cached plan that saves a 50-cent LLM call is a plan you never want to delete. The better caching works, the faster storage pressure grows, and NVMe/SSD tiers stop being optional and start being load-bearing infrastructure. RAM is not a trash can with a power button.

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