We’re trying to scale our Playwright test coverage across multiple browsers (Chrome, Firefox, Safari) and we’re experimenting with breaking the work into autonomous agent tasks. The theory is that if one agent handles test design, another coordinates browser launches, and a third analyzes results, we can run tests in parallel without everyone waiting on a single blocking step.
But I’m struggling with the practical choreography. When agent A designs a test, agent B needs to know it’s ready before starting the browser automation. When agent B finishes running the test on Chrome, agent C needs those results before it can analyze failures. If any agent is slow, the whole pipeline clogs up.
Right now we’re using manual handoff points—agent A publishes its output to a shared location, then agent B polls for it. It works, but it feels fragile. If a test takes longer than expected, agent C starts analyzing incomplete results.
Has anyone built a multi-agent test pipeline and actually gotten it to run smoothly without creating these artificial bottlenecks? What patterns actually worked for you?
This is one of those problems that looks simple on paper but gets messy fast when you’re coordinating across multiple systems. The polling approach you’re using is the classic bottleneck creator.
What actually works is event-driven handoffs instead of polling. Agent A completes a test design, and instead of B polling, A publishes an event that triggers B immediately. B runs the automation and publishes a result event that triggers C. No waiting, no polling.
The other piece is making sure agents have visibility into capacity. If agent B can’t start on all three browsers at once, it should queue them intelligently and let agent C know which results are coming when. This is where orchestration really matters.
Latenode’s Autonomous AI Teams feature is built exactly for this. You define agents for each stage of your pipeline, and instead of manual handoffs, you set up coordination rules between them. One agent completes its task, the next one is triggered automatically. The platform handles sequencing and ensures data flows smoothly.
You can also define retry logic and fallbacks—if an agent fails on one browser, it doesn’t block the others. The system’s smart enough to parallelize what can be parallel and serialize what needs to be.
Start with their no-code builder to map out your agent responsibilities, then customize with code if you need finer control.
I’ve dealt with this exact problem building test orchestration across multiple browsers. The polling approach you’re using becomes a nightmare at scale.
What helped me was moving to a message queue model. Instead of agents polling for work, they subscribe to channels. Test design agent publishes to a channel, automation agents listen and pull work immediately. Results go to another channel that the analysis agent consumes.
The key insight I had was separating concerns really clearly. Each agent has one job: design, automate on specific browser, or analyze. They don’t know about the other agents—they just know their input channel and output channel. This loose coupling meant I could add more browser automation agents without changing the design or analysis logic.
Also, I built in idempotency. If the analysis agent reprocesses the same result twice, it doesn’t break anything. This handles the “incomplete results” problem you mentioned—if results arrive out of order, it doesn’t matter because the system is designed to handle it.
The choreography problem you’ve identified is a classic distributed systems challenge. Your polling-based approach scales poorly because it introduces latency and increases system complexity as you add more agents or parallelism.
Implement asynchronous event-based communication instead. Design agents to emit completion events or status updates when they finish a task, rather than having downstream agents pull for results. This could be implemented via message brokers, pub-sub systems, or even simple database triggers depending on your infrastructure.
For your three-browser scenario, consider task-level granularity. Instead of waiting for all three browser tests to complete before analysis begins, have the analysis agent process results incrementally as they arrive. This enables true pipeline parallelism and reduces end-to-end latency significantly.
Also implement deadletter queues or timeout mechanisms so that if an agent fails, results don’t get lost and the pipeline can recover gracefully.
Use event-driven handoffs instead of polling. When agent A finishes, it triggers B immediately. B publishes events that trigger C. No waiting, no bottlenecks. Message queues work great for this.