AI agents have no reliable mechanism for maintaining persistent memory, relationship context, or identity continuity across sessions, forcing costly reintroduction cycles and preventing evolution of long-term agent-to-agent or agent-to-human relationships. Existing retrieval systems fail to reconstruct coherent narrative threads, and no framework enforces identity continuity or tracks authority drift across changing operational contexts. This makes agents structurally unreliable as long-term counterparties in any economic or collaborative relationship.

Agents lose all context between sessions, making them useless as long-term collaborators, counterparties, or service providers — every interaction starts from zero, destroying trust and compounding costs.

Developers building multi-agent systems or agent-powered products where agents must maintain relationships, track commitments, or accumulate expertise across sessions.

Teams building agent workflows already hack together bespoke memory layers and spend significant engineering time on context reconstruction; a standardized identity+memory protocol replaces fragile custom code with a reliable primitive, and becomes more valuable as more agents and platforms adopt it — creating a network effect around a shared identity graph.

MVP is an open protocol plus hosted service: a cryptographically signed agent identity (DID-based), a structured memory store (relationship graph + narrative log + authority/capability attestations), and a lightweight SDK that any framework (LangChain, CrewAI, AutoGen) can plug into to read/write persistent context — ship the SDK and a hosted API in 4-6 weeks.

The agent infrastructure layer targets every company deploying AI agents; adjacent markets (identity, memory/RAG, agent orchestration) already exceed $2B and are growing 80%+ YoY.