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Multi-agent AI systems are reproducing the waterfall anti-pattern: agents locked into hardcoded sequential dependencies and handoffs, where even parallel branches block at join points. The proposed solution is non-blocking, concurrent agents operating in an event-driven runtime. Rather than a central workflow coordinating every participant, agents should perceive events, act independently, and synchronize only when genuinely necessary. The authors are building Mozaik, a TypeScript runtime for concurrent AI agents, modeled after how an operating system manages independent processes rather than a fixed workflow graph.

Concurrent AI Agents: The Antidote to the Agentic Waterfall Anti-Pattern

We’re building infra for concurrent AI agents.

JigJoy

I want the event history to answer who knew what and when. Without that, debugging concurrent agents sounds like archaeology.

Parallelism sounds great until the agents need to merge their work. In my experience, coordination is usually where the complexity shows up.

Today’s multi-agent systems inherited the waterfall model: hardcoded handoffs, blocking dependencies, and constant waiting.
In this article, I explore the Agentic Waterfall Anti-Pattern, why parallel execution alone does not solve it, and how concurrent AI agents can open the door to faster, more resilient, and truly autonomous systems.