I’ve been reading about Autonomous AI Teams—basically having an AI CEO plan the workflow and an AI Analyst validate the results. The concept really appeals to me for webkit scraping because it’s complex: you need planning, execution, error handling, and validation. Four separate concerns that could work well with different agents.
But I’m struggling to understand how this actually plays out in practice. Does the AI CEO hand off to the scraper, which hands off to the validator? And if the validator finds an issue, does it loop back to the CEO to replan? Or does it just flag the problem?
My concern is that coordinating multiple agents sounds elegant in theory but might introduce a lot of failure points in reality. If agent A makes a mistake, does agent B catch it or propagate it? And how do you prevent agents from getting into loops or stuck states?
I’m also wondering about the overhead. Is managing multi-agent orchestration worth the added complexity, or would I be better off just building a single, more sophisticated workflow?
Has anyone actually deployed this for webkit work and gotten it to run reliably over time, or is it still more experimental?
Multi-agent coordination works when you design it right. The AI CEO doesn’t just hand off and disappear—it stays aware of what’s happening and can adjust the plan if something goes wrong. The Analyst validates the output and feeds back to the CEO if there’s an issue.
The key is clear interfaces between agents. Define exactly what data each agent receives and produces. If the Analyst finds bad data, it should tell the CEO specifically what failed, not just flag it as broken.
I’ve built webkit scrapers with this pattern. The CEO handles site-specific logic like “this site uses infinite scroll, so we need to scroll before scraping”. The scraper executes that plan. The Analyst verifies the data quality. It’s slower than a single agent blindly trying to scrape, but way more reliable.
On Latenode, you can set up these coordination patterns without writing complex orchestration code. The platform handles agent communication. That’s where the real value is—you focus on the logic, not the plumbing.
I tried this approach and hit the same concern—too many moving parts. But then I realized the issue was my design. I was trying to make the agents too autonomous. What actually worked was giving each agent a very specific, limited job and clear success criteria. The CEO decides if we scroll or not. The scraper just follows orders. The Analyst checks if the data schema matches expectations. When you constrain them like that, they don’t get stuck.
Multi-agent scraping does work, but it requires thinking differently about error handling. Instead of one agent trying to handle everything and failing, you have specialized agents that fail gracefully. The CEO sees the failure, adjusts the plan, and tries a different approach. I’ve used this for scraping sites that break frequently. When the Analyst catches a data quality issue, the CEO reruns with different parameters. It’s more robust than I expected, though definitely slower than a single-agent approach.
Multi-agent scraping is reliable when agents can’t interfere with each other. Separate concerns, clear data contracts between agents. Design for failure at each boundary.