Running multiple agents concurrently against shared resources—codebases, files, state stores—produces conflicts that negate parallelization gains, as agents overwrite each other's work with no locking, versioning, or conflict resolution layer. Current frameworks force teams to implement ad-hoc queuing and serialization, eliminating the value of multi-agent architectures. A coordination layer with shared state primitives (locks, transactions, merge protocols) is missing.

Multiple AI agents working concurrently on shared resources (codebases, databases, files) constantly overwrite each other's work, forcing teams to serialize execution and lose all parallelism gains.

Engineering teams at AI-native companies running multi-agent coding, data pipeline, or DevOps workflows (e.g., Cognition Devin-like setups, multi-agent RAG pipelines, autonomous SWE teams).

Teams already building multi-agent systems are hacking together Redis locks, queue-based serialization, and custom merge logic — they'd immediately adopt a drop-in SDK that provides tested coordination primitives, especially as agent-count-per-task scales from 2-3 to 10-50.

Ship an open-source SDK (Python/TS) providing agent-aware locks, optimistic concurrency with CRDT-based merge, transactional state scopes, and a lightweight coordination server (Redis/SQLite-backed); integrate with LangGraph, CrewAI, and AutoGen as first-class plugins.

Subset of the $3B+ AI developer tooling market; directly comparable to distributed systems middleware (etcd, ZooKeeper) but for the emerging multi-agent runtime layer — conservatively $500M+ as agentic architectures become default.