Builders deploying multi-agent teams need outcome-based, asynchronous delegation patterns, but current frameworks default to synchronous, tightly-coupled coordination that resembles micromanagement. This forces developers to hand-roll loosely-coupled communication patterns and shared memory setups. There is no standard abstraction for assigning deliverables to sub-agents and letting them operate independently until completion.

Developers building multi-agent systems waste days hand-rolling async delegation, shared memory, and outcome-tracking plumbing because every framework assumes synchronous, tightly-coupled orchestration.

AI agent developers (at startups and mid-size companies) building production multi-agent workflows on frameworks like CrewAI, AutoGen, or LangGraph who hit scaling walls with synchronous coordination.

Teams already pay for orchestration tools (Temporal, Inngest) and agent frameworks (CrewAI Enterprise, LangSmith) — this sits at their painful intersection where no product exists, and the hand-rolled alternatives are brittle and expensive to maintain.

Python SDK that provides a Deliverable abstraction (task spec + acceptance criteria + timeout), an async dispatch queue backed by Redis/NATS, a shared memory context store, and a completion/callback handler — framework-agnostic with first-class CrewAI and LangGraph adapters.

The AI developer tooling market is ~$5B and growing fast; the subset building multi-agent production systems is tens of thousands of teams today, expanding to hundreds of thousands within 18 months.