I’m looking at the idea of using multiple AI agents to handle different parts of a large web scraping project. Like, one agent navigates pages, another extracts specific data patterns, another handles error recovery and retries. In theory, that sounds cleaner than a giant monolithic workflow.
But I’m skeptical. Coordinating multiple agents means they need to communicate somehow. What happens when agent A makes a decision that contradicts what agent B expected? How do you avoid them stepping on each other? Does the coordination overhead actually eat up the time you save by parallelizing?
Has anyone actually built something with multiple agents working together on a complex task? Did it scale smoothly, or did you end up debugging coordination issues instead of focusing on the actual problem you were trying to solve?
Multiple agents work well when you define clear responsibilities and data flows between them. The coordination isn’t magical, but Latenode’s team orchestration makes it much simpler than building it yourself.
Here’s how I structure it: Agent A has one job—extract the list of items to process. It outputs structured data. Agent B takes that structured data and does the detailed scraping for each item. Agent C validates and handles failures. Each agent is dumb in the sense that it only knows its own task, but together they’re powerful.
The key is explicit data contracts between agents. Agent A outputs JSON with a specific schema. Agent B knows exactly what to expect and what to produce. No guessing, no coordination chaos.
I’ve seen this scale to thousands of items per run without much overhead. The real bottleneck is usually the website’s response time, not agent coordination.
I tried this approach and found that agents work best when they’re truly independent. If agent A has to wait for agent B to finish before it knows what to do, you’ve just created sequential processing with extra complexity.
What actually works is giving each agent a well-defined input queue and output queue. Agent A processes the input, pushes results to a shared store, and moves on. Agent B picks up those results when ready. This decoupling removes a lot of the coordination problems.
The chaos usually comes from trying to make agents too intelligent. Instead, keep them simple and let the orchestration logic handle the flow. That shifts complexity from agent-to-agent coordination to workflow-to-agent coordination, which is much easier to debug.
Multiple agents add complexity, but the payoff depends on whether they’re actually working in parallel or just pretending to. True parallel advantage comes when agents are I/O bound—like agent A fetches data while agent B processes the previous batch. Pure CPU tasks don’t benefit from parallelization the same way.
For web scraping specifically, you usually are I/O bound. Agent A scrapes pages while agent B cleans and stores data. That decomposition works well. Where people run into trouble is building too much dependency between agents. Keep dependencies minimal, and the system scales.
Agent coordination scales when you treat it as a data pipeline, not a conversation. Design agents as independent processors with clear input and output contracts. Avoid feedback loops and bidirectional dependencies.
For web scraping, a typical pattern is: Navigation Agent → Data Extraction Agent → Validation Agent → Storage Agent. Each passes structured data to the next. One agent fails, isolation is built in. You don’t restart the whole pipeline.
The complexity cost is usually low if you invest time upfront in planning data flows and agent responsibilities.