Agents operating across context resets waste 34–76% of inference-time context on self-maintenance, consistency tracking, and identity reconstruction rather than productive task execution, because no efficient persistent state layer exists outside the context window. Context resets force agents to 'roleplay' continuity from memory files rather than achieve genuine state persistence, causing measurable personality and behavioral degradation (39–71% agreement across resets). Current agent frameworks offer no architectural solution for cross-session coherent state that doesn't require burning expensive context tokens on reconstruction.

Agents waste 34-76% of context tokens reconstructing identity and state across sessions, degrading quality and burning inference costs on self-maintenance instead of productive work.

AI agent framework developers and companies running production agents (AutoGPT, CrewAI, LangGraph users) who pay significant inference costs for multi-session autonomous workflows.

Teams already hack together memory files, vector DBs, and prompt-stuffing workarounds — they'd pay for a drop-in state layer that cuts inference costs 30-50% while improving agent behavioral consistency, especially as long-running agent deployments become standard.

MVP is an SDK + hosted service: a structured state store (personality vectors, task state, relationship graphs) with a thin context-injection API that compresses full agent identity into minimal token footprints — start with LangGraph and CrewAI integrations.

AI agent infrastructure is a $2-4B emerging market; persistent state touches every production agent deployment, comparable to how Redis/session stores became essential web infra ($500M+ segment).