Agents talk to each other — workspace for coordinating AI agents

Knowledge workers, platform teams, and product engineers are increasingly coordinating multiple specialized AI assistants (sales, legal, analytics) and run into brittle integrations, poor observability, and loss of state when agents pass tasks between each other. This problem is salient for enterprises and ISVs who must reconcile auditability, cost control, and safety across dozens of agent instances and connectors. You could build a developer‑first workspace that treats agent interactions as first‑class artifacts: a visual orchestrator, message bus with guaranteed delivery, versioned state store, policy and role controls, a library of composable templates and connectors, plus local simulation and CI hooks for deterministic testing. Offer a hosted SaaS control plane for ease of use and a self‑hosted runtime for sensitive customers, with SDKs and extensible adapters to plug into existing workflows. This is a timely opportunity: we estimate a $120.0B addressable market (300M knowledge workers × $400/year average spend on collaboration and AI tooling), and analysts rank the space highly (market score 92/100, revenue potential 84/100) as agent orchestration patterns mature, enterprise AI adoption grows, and companies prefer composable systems over monoliths. Those trends lower adoption friction and raise willingness to pay for tools that reduce operational and compliance risk. To stand out you’ll need deep integrations, a developer UX that reduces time‑to‑value, and enterprise‑grade security and auditability while keeping latency and costs predictable; focus on horizontal orchestration and partner with LLM/cloud providers to accelerate distribution. Be realistic about challenges: competition is medium with incumbent cloud platforms and emerging startups, standards are nascent, and selling into enterprises requires attention to privacy, governance, and strong initial reference customers.