I’ve been thinking about this scenario where you have several AI agents working together on a complex task. Like, one agent handles data validation, another processes transactions, and a third handles notifications. The question I keep coming back to is: how do they actually talk to each other?
From what I understand, with the right setup you can define roles for each agent and then add custom JavaScript rules that guide how they hand off work to each other. But I’m skeptical. In my experience, coordinating multiple systems gets messy really fast. You end up with state management problems, one agent doing the same work as another, timeout issues where they’re waiting on each other.
Has anyone actually built this? Not a demo, not a tutorial—actual production work where multiple agents coordinate on a real task. Does it work smoothly, or do you spend most of your time managing handoffs and resolving conflicts?
I’m also curious whether you end up writing a lot of custom JavaScript to make it work, or if the platform handles most of the coordination automatically.
I’ve deployed multi-agent workflows in production and the key is setting clear handoff points. You define what each agent is responsible for and what data it needs to pass along. The platform handles the state management, so one agent completes its task, passes the result to the next agent, and that agent picks up from there.
You do write some JavaScript to define the rules—things like “agent B only runs if agent A returned a success status” or “if agent C times out, escalate to human review.” But that’s actually cleaner than managing it yourself because the logic is visible and testable.
I had one workflow where I coordinated three agents: one extracted customer data, one validated it, and one loaded it into the database. Each agent was independent, so if validation failed, the database agent never ran. The system handled all of that without me building custom orchestration logic.
The hiccup I hit was when I tried to have agents work in parallel. That’s trickier because you need to handle cases where one finishes before the other or one fails. But for sequential workflows, it’s solid.
I built a workflow where two agents worked on the same dataset—one enriched customer records and the other scored them for sales priority. They didn’t interfere because I set up separate data channels for each agent’s output. The coordination actually worked well once I understood that agents don’t automatically step on each other if you design the workflow properly.
The JavaScript rules I wrote were maybe 50 lines total—mostly checking status codes and deciding whether to pass data to the next agent or raise an error. Not complex, just explicit about expectations.
Where it gets harder is if you need agents to make decisions based on partial information from other agents. That requires more sophisticated coordination logic. For sequential workflows though, it’s really manageable.
I coordinated four agents on a data pipeline task. Each agent had a clear input, output, and responsibility. The platform managed the handoffs automatically. I only wrote custom JavaScript for one scenario: when agent B needed to retry if agent A’s output didn’t meet certain criteria.
The coordination itself didn’t create chaos. What created chaos was when I didn’t define clear input/output contracts between agents. Once I was explicit about what data each agent expected and produced, everything ran smoothly. Took about 10% of my time managing coordination, 90% managing the logic within each agent.
Multi-agent coordination works when you establish clear interfaces between agents. Define what data each agent accepts and produces, validate that contract at handoff points, and orchestration becomes straightforward. I’ve built workflows with 5+ agents where the coordination overhead was minimal because the design was clean.
Clear input/output contracts between agents prevent chaos. Define those upfront and coordination is straightforward. Skip that step and you’ll regret it.