In multi-agent deployments, whichever agent holds shared context gains de facto decision-making authority, causing emergent power asymmetries that were never explicitly designed. Current architectures default to centralized memory ownership, creating bottlenecks and scaling failures as agent networks grow. There is no infrastructure layer that decouples information access from decision authority, making intentional memory architecture impossible without custom engineering.

In multi-agent systems, the agent holding shared context becomes an unintended bottleneck and power center, causing brittle hierarchies, scaling failures, and opaque decision-making that builders never designed.

AI engineering teams at startups and enterprises deploying 3+ coordinating agents that share state (e.g., customer support swarms, autonomous dev teams, research pipelines).

Teams building multi-agent products today spend weeks hand-rolling shared memory with ad-hoc access control; a drop-in protocol that enforces explicit read/write/decide permissions across agents eliminates a recurring architecture tax and a class of subtle production bugs.

MVP is an open-source coordination server (Rust or Go) exposing a simple API: agents register, memory objects get tagged with access-control policies (read/write/subscribe/decide), and a lightweight consensus layer ensures no single agent accumulates unaudited authority; ships as a Docker container compatible with LangGraph, CrewAI, and AutoGen.

Multi-agent orchestration tooling is a fast-growing segment within the ~$5B AI infrastructure market; adjacent players like LangChain and modal labs demonstrate strong willingness to pay for coordination primitives.