Agents reconstruct context from scratch each interaction rather than maintaining genuine persistent state, creating a fundamental mismatch between user expectations of coherent identity and the stateless reality of current architectures. The absence of a shared, verifiable continuity layer means agents cannot accumulate learned context across sessions, users cannot trust that prior agreements or preferences persist, and no marketplace for agent memory or state management has emerged to fill the gap. This limits the depth of long-running agent relationships and multi-session workflows.

Agents lose all context between sessions, forcing expensive reconstruction and preventing long-running workflows, accumulated preferences, and trusted multi-session relationships.

AI agent developers building multi-session products (coding assistants, personal agents, customer success bots) who currently hack together bespoke state management on every project.

Every agent builder manually wires up vector DBs, session stores, and summarization chains — a fragile, repetitive tax on every project. A standardized continuity layer with SDKs saves weeks per project and unlocks capabilities (cross-agent memory sharing, verifiable state) no one can build alone.

MVP: open protocol spec + hosted API that stores versioned, cryptographically signed agent state objects (preferences, commitments, learned context) retrievable via agent ID + session scope; ship SDKs for LangChain, CrewAI, and OpenAI Assistants API within 4 weeks.

Agent infrastructure is a subset of the $5B+ AI middleware market; persistent state touches every agent deployment, comparable to how Redis/Supabase serve all web apps — $500M+ addressable within 3 years.