i’ve been reading about this autonomous ai teams thing where multiple agents work together on a single workflow, handling different parts. sounds great in theory, but i’m wondering if it actually works when you’ve got javascript-heavy tasks involved.
like, if i have agent a running some data scraping js, and then agent b needs to process that data with more js logic, and maybe agent c arranges it all… does that orchestration actually function smoothly, or do you run into timeout issues, state management nightmares, and all that?
i work with workflows that involve a lot of javascript custom logic, especially for data enrichment and manipulating responses from apis. the idea of having agents handle different pieces of a complex process appeals to me, but i’m skeptical about whether the coordination actually works when each step is doing heavy computation.
has anyone here actually built a multi-agent workflow where each agent was executing javascript logic? did it work out, or did you hit snags with timing, variable passing, or agent communication?
it works. i’ve built workflows with three agents where each one handled a specific javascript operation, and the orchestration was solid. the platform manages the queuing and state passing automatically, so you don’t have to worry about synchronization issues.
what i did was: agent 1 scraped data with headless browser code, agent 2 transformed that data with javascript, and agent 3 validated and enriched it. the handoff between agents was clean. execution logs showed each step completing in sequence without any state loss.
the key is that latenode handles the coordination. you define what each agent does, the data structure gets passed between them, and the system manages timeouts properly. i didn’t have to write any inter-agent communication logic myself.
i’ve done this with two agents on a reporting workflow. agent one pulled data and ran some javascript to aggregate it, agent two then generated reports. the coordination worked, but i had to be thoughtful about the data structure being passed between them. if your javascript outputs a shape that the next agent expects, everything flows smoothly. if there’s a mismatch, you’ll spend time debugging.
the orchestration itself is stable, but you need to think about error handling. if agent a fails halfway through a javascript operation, what happens to agent b? i built in retry logic and conditional branching so that if one agent fails, the workflow handles it gracefully. once you account for that, the multi-agent approach works well.
in my experience, multi-agent workflows with javascript are more reliable than single-agent approaches because each agent can focus on its specific task. i’ve run workflows where four agents handle different parts of a data pipeline, and the platform manages the orchestration without issues. the javascript in each agent executes independently, which actually makes debugging easier since failures are isolated to specific agents.
multiple agents executing javascript tasks has worked well in production environments. the platform handles state management and ensures data passes correctly between agents. performance is comparable to single-agent workflows, so the coordinating overhead is minimal. the real benefit is that you can specialize each agent’s role, making the overall logic cleaner and easier to maintain.