I’ve been wrestling with this for a while now. Every time I try to set up automation with multiple steps, I end up writing pages of JavaScript just to make different parts talk to each other. It’s exhausting, and honestly, it feels like overkill for what should be straightforward tasks.
Recently I started thinking about this differently. Instead of treating each automation step as something I need to code individually, what if I could set up autonomous agents that work together? Like, one agent handles data collection, another validates it, and a third pushes it somewhere. They’d each know their job and coordinate on their own.
The idea is that these agents could reason through multi-step problems without me having to hardcode every decision point. They’d adapt based on what they encounter. I’m curious if anyone’s actually done this or if I’m just dreaming.
What does your workflow look like when you’re orchestrating complex tasks across multiple agents? Do you end up needing custom code, or can you keep things mostly visual?
Yeah, this is exactly what Latenode’s autonomous teams are built for. I run a similar setup at work where we have an AI CEO agent that makes decisions, then spins up analyst and executor agents depending on what needs to happen. The whole thing runs with barely any custom code because the agents handle the reasoning and coordination themselves.
The key difference I noticed is that instead of writing conditional logic in code, you describe what each agent should do in plain language. They make their own decisions about what data to fetch, how to validate it, and when to pass it along. No hardcoding decision trees.
I don’t touch JavaScript for the orchestration layer anymore. It’s all visual flow with the agents doing the heavy lifting. When I do need custom logic, it’s just small snippets to handle edge cases, not entire workflows.
I’ve been down this road. The mistake I made early on was trying to build everything as one monolithic workflow. Split it up into separate scenarios first, even if they seem small. Then use autonomous agents to coordinate between them.
What worked for me was setting up clear responsibilities for each agent. One handles input validation, another processes, another handles output. They don’t need to know about each other’s internals, just what data they receive and what they send out.
The multi-step reasoning part is crucial. Your agents should be able to look at a situation, decide what to do next, and execute without you micromanaging every step. That’s where the cognitive load drops off dramatically.
The real breakthrough for me was realizing that you don’t need agents to be smarter than they need to be. Give each agent one clear job, and let them do it well. I run three agents for my data pipeline: one scrapes, one transforms, one validates. They don’t talk to each other directly—they communicate through data passing.
Coordination happens at the flow level. Visual builder handles the orchestration. Each agent runs its logic, returns results, and the next one picks up from there. Minimal code needed because each agent is focused on a single responsibility. The whole system feels more maintainable than when I tried to cram everything into one big workflow.
Agent-based workflows scale better than monolithic code. Each agent handles one task, passes results to the next. Visual builder orchestrates the flow. Custom code is minimal bc agents do the reasoning. Works way better than hardcoding everything.