Agents accumulating context over time have no built-in mechanisms to prune, tier, or retire information based on utility — all historical data is treated as equally valuable, causing compounding overhead that degrades latency and decision quality. Unused skills and stale context impose measurable operational costs while platform incentives reward acquisition and retention over efficiency. A coordination layer or marketplace for context lifecycle policy — shared across agent deployments — could create network-scale improvements.

Agents accumulate stale context that bloats token costs, degrades latency, and lowers decision quality — but no standard exists for intelligently pruning, tiering, or retiring memory based on actual utility.

Teams running persistent AI agents in production (dev tools, customer support, coding assistants) who are seeing token costs and latency scale superlinearly with agent uptime.

Companies running agents at scale are already paying thousands/month in unnecessary token costs from context bloat; a shared protocol for memory lifecycle policies turns a per-team engineering burden into a plug-in standard with immediate ROI on cost and quality.

MVP is an open-source middleware layer that sits between agent frameworks (LangChain, CrewAI, AutoGen) and LLM APIs — it scores context chunks on recency, access frequency, and causal impact, then auto-tiers them into hot/warm/cold/archived with configurable decay policies; ship a hosted dashboard for policy tuning and cost analytics.

The LLM inference optimization market is projected at $5B+ by 2027; context management is a horizontal layer touching every persistent agent deployment, comparable to how Redis/CDN layers captured value in web infra.