we’ve got this project where we need to scrape a site, validate the data, handle logins, and then structure everything. it’s a multi-step process that’s always been error prone because one break in the chain breaks everything.
i’ve been thinking about what it would look like if different agents could each own a piece of this: one handles the login and navigation, another focuses on extraction, another validates and structures the data. in theory, they coordinate and handle failures independently.
but i’m skeptical. does having multiple agents actually simplify things or does it just push the complexity around? like, now instead of debugging one brittle workflow, i’m debugging communication between agents.
has anyone actually gotten this working reliably? or does it end up being more overhead than it’s worth?
you’re thinking about this exactly right, and the answer is yes, it actually works. the coordination becomes invisible when you set it up properly.
think of it this way: each agent owns one responsibility. the login agent doesn’t care about extraction. the extraction agent doesn’t care about validation. they pass data between each other. if one fails, the others don’t cascade. that’s the win.
latenode handles the orchestration between agents, so you’re not building custom retry logic or error handling. you describe what each agent does, and the platform manages handoffs. the complexity is gone because it’s abstracted away.
what people find is that this is actually simpler than one monolithic workflow. debugging becomes easier because you isolate problems to specific agents.
i’ve worked through this exact concern. the trick is understanding that agent coordination isn’t about creating new complexity, it’s about dividing the problem into pieces that can fail independently.
what makes it work is that each agent is focused. one handles authentication and staying logged in. that’s its only job. it doesn’t try to extract data or validate anything. same with the others. when you keep scope tight, coordination becomes straightforward.
the real benefit i’ve seen is error isolation. before, if extraction failed, you had to restart the whole workflow. now extraction failure doesn’t affect authentication. you restart just the extraction step. saves enormous amounts of time on long-running processes.
from my experience, the complexity concern is valid but misplaced. yes, you’re introducing coordination between agents, but that’s actually less complex than managing state across one giant sequential workflow. I’ve found that when each agent has a single responsibility, debugging becomes significantly simpler because you can isolate problems. The handoff between agents is straightforward message passing. You validate that agent A completed successfully before agent B starts. That’s it. The real reduction in work comes from not having to rerun the entire extraction pipeline when validation fails halfway through. You just restart the validation agent and skip the expensive extraction step.
Multiple agent orchestration actually reduces work substantially when implemented correctly. The key is proper state management and clear handoff points between agents. Each agent operates autonomously, which means failure in one doesn’t cascade to others. You also gain the ability to parallelize steps that were previously sequential. From a practical standpoint, I’ve seen organizations cut automation maintenance time by half because issues become isolated to specific agents rather than requiring full workflow debugging. The coordination overhead is minimal compared to the debugging savings.
worked with this approach. coordination is simple when agents are focused. each handles one thing. big win: failures dont cascade. extraction breaks? restart just extraction, not everything. thats why its actualy easier than one big workflow.