I’m working on a project where I need to scrape data from about five different e-commerce sites, and each one has a slightly different structure, login process, and data layout.
My first instinct was to build five separate workflows, one for each site. But that feels wasteful. I keep thinking there should be a way to have specialized agents or workflows that can work together.
Like, what if I had one agent that handles authentication across all sites, another that focuses on navigation, and a third that handles data extraction? They could coordinate somehow?
The challenge is that sites have unique quirks. The login flow on site A is completely different from site B. The data I need might be in a table on one site but scattered in divs on another.
I’m wondering if there’s a pattern for this where you have a coordinator agent or workflow that delegates to specialized sub-workflows, with clear handoffs between them. Or would that create too much overhead and it’s just better to keep them separate?
This is exactly what multi-agent coordination is designed for, and it’s way more efficient than separate workflows.
The pattern you’re describing is called agent orchestration. You have a coordinator that understands the overall task (“scrape these five sites”), and it delegates to specialized agents. Each agent focuses on what it does best.
Here’s the flow: coordinator agent receives the task and site list. It delegates to a site-specific authentication agent that handles login for each individual site (the logic adapts based on which site it’s processing). Once authenticated, a navigation agent takes over and finds the right data pages. Finally, an extraction agent pulls the information.
The overhead is minimal because the delegation is handled through the platform. The agents pass structured data to each other, so there’s no manual context switching.
What makes this better than five separate workflows is maintainability. If site A changes its login process, you update the authentication logic in one place. New sites can be added by updating the coordinator’s site list, not by creating entirely new workflows.
Latenode has Autonomous AI Teams that handle exactly this. You set up specialized agents, and they coordinate automatically. The platform manages the handoffs and data passing between agents. It’s clean, scalable, and way less brittle than parallel separate workflows.
I tackled something similar with three different vendors’ APIs that had different authentication and response structures.
What I learned is that coordination overhead is real if you’re not structured well. The key is clear contracts between agents. Agent A outputs structured data in a specific format, and Agent B knows exactly what to expect.
For your use case, I’d separate concerns like this: one module handles site-specific configuration (login URL, selectors, data structure). Another module does authentication. Another does navigation. Another extracts data. The coordinator orchestrates them with the configuration as context.
The benefit is that when site B changes its login page, you only update the configuration. The authentication module itself stays the same. When you add site six, you just add a new configuration block.
It does require upfront thinking about what data flows between agents, but once that’s clear, the system becomes really stable.
Coordination works if you have clear responsibilities. The overhead comes when agents are too tightly coupled or when data passing is messy.
I’d recommend starting with a primary coordinator workflow that handles branching based on site id, then calls specialized sub-workflows for each phase (auth, navigation, extraction). This way you get the benefits of specialization without complex inter-agent communication.
The trick is error handling. If authentication fails on one site, the coordinator needs to know that and either retry with different credentials or skip to the next site. Make sure your coordination logic includes proper error paths.
Multi-agent coordination for this use case follows an actor model pattern. A coordinator receives tasks and spawns specialized agents as needed. Each agent is stateless and idempotent, allowing for retries without side effects.
The critical design decision is message passing. Implement explicit schemas for what each agent expects as input and what it produces as output. This decoupling lets you modify individual agents without cascading failures.
For heterogeneous sites, a strategy dispatcher based on site type reduces complexity. Rather than one universal extraction agent, you have site-type-specific extraction agents that know how to parse that particular site’s structure.