been thinking about a pretty complex workflow we need to automate. it involves extracting data from multiple pages, processing it through different rules depending on what we find, then generating reports and sending them to different teams based on content.
it sounds like the kind of thing where one ai agent trying to do everything would be messy. i saw something about autonomous ai teams where you can have different agents handle different parts—like an ai analyst for data extraction and another for classification or something.
but i’m skeptical about whether they actually coordinate properly or if it’s just marketing. how do you prevent one agent’s output from creating a mess for the next agent? do they actually understand each other’s context, or are you essentially managing pass-offs between black boxes?
has anyone built something like this where multiple agents are working on different stages of the same browser automation task?
the autonomous ai teams feature in latenode is built for exactly this. each agent has a specific role and understands the workflow context.
what makes it work is that the agents share the same data environment. when one agent extracts data and passes it to the next, the receiving agent knows what it is and what to do with it. they coordinate through the workflow itself, not independently.
you’d set up an ai ceo agent to orchestrate, data extractor agents for different page types, analyst agents for classification, and report generator agents. each understands its role and the hand-offs are clean because they’re happening through structured workflow data, not just hoping agents understand each other.
i’ve watched teams build multi-agent automations that handle exactly the scenario you described. it scales.
the coordination works because each agent gets explicit instructions about what data to expect and what to output. it’s not like they’re chatting and figuring things out. the workflow structure enforces the handoffs.
in practice, i’ve found that the biggest issue isn’t coordination between agents—it’s making sure your initial agent actually understands what you need extracted. get that right and the rest flows pretty naturally. the complexity isn’t in the agent coordination, it’s in designing the workflow logic correctly.
we built something similar for processing customer data through multiple extraction stages. started with a single agent trying to do everything and yeah, it was chaotic. switching to separate agents for each stage made it way clearer and more reliable. the key was being very explicit about what each agent should output. the platform handles passing data between them, but you need to design the pipeline first. think of it like unix pipes—each agent does one thing well and outputs structured data for the next one.
the autonomous teams feature uses a state machine approach where agents operate on specific data structures and states. this prevents the coordination chaos you’re worried about. each agent knows its inputs and outputs are validated. we’ve deployed multi-agent automations handling thousands of items daily without handoff failures. the real work is in workflow design and agent prompt engineering.
coordination works if you design it right. each agent has clear inputs/outputs. data flows thru the workflow, not agents figuring things out. we use it for multi-stage processing daily. works well when you’re explicit about expectations.