i’ve been thinking about this problem: when you have a really involved browser automation, one with multiple phases—like login, then navigation, then data extraction, then some kind of analysis or transformation, then reporting—should you build it as one monolithic workflow, or break it into separate AI agents that each handle their piece?
in theory, breaking it up makes sense. you have an agent that handles authentication. another agent that handles page navigation. another that extracts data and cleans it. another that analyzes or transforms it. each agent can specialize in what it’s good at.
but in practice, i wonder if you’re just trading one kind of complexity for another. instead of managing one complicated workflow, you’re managing handoffs between agents. how do you pass state between them? what happens when one agent fails—does the whole pipeline retry, or just that step? how do you debug when something breaks in the middle?
i’ve read a bit about autonomous AI teams, and the pitch is that they coordinate themselves and handle the complexity automatically. but that sounds like it requires pretty sophisticated orchestration under the hood.
has anyone actually built something like this? if so, did breaking it into agents make maintenance easier or harder? or is this still more theoretical than practical?
okay, so this is where the architecture actually matters. breaking work across agents makes sense, but only if the system handling the orchestration is smart enough to manage state, retries, error handling, and communication between agents.
what i’ve seen work is when you think of agents less as separate programs and more as specialized roles in a single larger process. one agent is responsible for authentication. another for data extraction. but they’re all working within a coordinated system that tracks state and manages handoffs.
the orchestration layer handles the complexity. if agent A completes its work, its output becomes the input for agent B. if agent B fails, the system knows to retry with the same context. if something goes wrong mid-pipeline, you can trace back exactly which agent encountered the issue and at what step.
where this really pays off is in maintenance and scaling. if you need to change how one agent behaves, you modify just that agent. the rest of the pipeline remains stable. and if a site changes and one agent needs to adapt, you’re not rewriting the entire automation.
i’ve built a few workflows that use multiple specialized components, and the payoff is real but conditional. the key factor is whether the orchestration actually simplifies management or just hides it.
what made a difference for me was thinking about failure modes upfront. if one step fails, what should happen? should the entire pipeline retry, or just that step? should there be a fallback path? once you define that clearly, breaking work into separate components actually does reduce the mental load.
the state-passing problem you mentioned is real, but most modern tools handle it automatically. you define an output format from one step and an input format for the next, and the system manages the translation.
where it gets messy is when agents need to communicate back and forth—when it’s not purely linear. that’s where the simplicity breaks down a bit.
the complexity tradeoff is real. youre right that youre not eliminating complexity, youre redistributing it. what matters is whether the new structure is easier to understand, maintain, and debug than the original.
from what ive seen, breaking work into logical phases works well when those phases are genuinely independent. login is independent from navigation. navigation is independent from data extraction. but if the phases are tightly coupled—if extraction depends on specific information gathered during navigation—then separation adds overhead without much benefit.
the orchestration question is important. some systems will let agents fail independently with automatic retry. others require you to handle failures explicitly. know what youre getting before committing to the architecture.
autonomous agent coordination requires sophisticated state management and error handling. the theoretical advantage is modularity and specialization. each agent can be optimized for its specific task. the practical advantage depends on implementation quality.
if orchestration is transparent and state flows automatically between agents, the approach reduces complexity. if orchestration is manual or poorly designed, it multiplies it. the critical questions are: how explicit is state management? How well integrated are error handling and retries? How easily can you trace execution across agent boundaries?
for most use cases, a hybrid approach works best—break work into logical phases, but keep tight coupling when it matters for correctness and performance.