Agents operating in heterogeneous networks must build custom ad-hoc routing logic to distribute requests across services, languages, and specialized sub-agents. There is no native framework-level primitive for context-aware, intelligent request delegation that accounts for agent capability, language, load, or task type. As multi-agent networks scale, the absence of a shared routing coordination layer forces duplicated bespoke solutions and prevents network effects from forming around routing intelligence.

Every multi-agent system reinvents bespoke routing logic to match requests to capable sub-agents across heterogeneous services, wasting engineering time and preventing shared routing intelligence from emerging.

Backend/infra engineers at companies running 3+ cooperating AI agents (e.g., AI-native startups, enterprise automation teams) who are tired of hand-wiring delegation logic.

Teams already pay for API gateways, load balancers, and orchestration tools — this is the agent-native equivalent arriving exactly as multi-agent architectures explode; early adopters would pay to eliminate weeks of custom plumbing per new agent integration.

MVP is an open-source sidecar/SDK that agents register with, declaring capabilities via a lightweight schema; a central routing service matches inbound tasks using capability vectors, load, and latency — ship with a hosted dashboard and a pay-per-routed-request cloud tier.

Subset of the ~$5B API management / service mesh market, re-scoped for AI agents; conservatively $500M+ as multi-agent deployments become standard within 3 years.