We’re tasked with pulling competitive intelligence from a bunch of webkit-rendered sites, and it’s a mess doing this manually. I’m talking about tracking pricing, feature updates, messaging changes—all the things CEOs care about for strategy meetings.
The thing is, these sites are all different. Some are single page apps, some have heavy JavaScript, some use dynamic rendering. We need someone to scrape the data, someone to make sense of it, and someone to package it into something a CEO would actually read.
Right now it’s just us doing everything ourselves, and it’s slow. Finding better ways to coordinate this work—like having specialized tools that can divide and conquer—would be huge.
Is anyone actually doing this at scale? How do you make it work without hiring a full analyst team just to track competitors?
You’re describing a perfect use case for Autonomous AI Teams. Think of it like staffing a small team without the overhead.
You’d set up an Analyst agent that knows how to navigate these webkit sites and extract the specific data you care about. Then a Reporter agent that takes that raw data and turns it into executive summaries. They work together, divide tasks, and output clean dashboards ready for your CEO.
The advantage is that each agent is purpose-built for its job, and they coordinate without you having to wire everything together manually. Updates happen automatically on a schedule, so you’re not constantly rerunning things.
I’ve been doing something similar with e-commerce market tracking. The turning point for us was stopping trying to do everything in one workflow and instead building separate, focused processes. We have one that handles the raw extraction, another that cleans and validates, and a third that generates the summary.
When you split the work up like that, each piece becomes simpler to maintain and more reliable. Plus, when one thing needs updating, you’re not touching the whole chain. The coordination overhead sounds annoying at first, but it actually reduces errors and makes things more predictable.
Orchestrating multiple specialized agents for data collection is a solid pattern. What matters is that each agent has a clear responsibility. One for data gathering, one for processing, one for presentation keeps the logic separated and easier to debug when something breaks. Most teams underestimate how much coordinating these steps saves them in the long run, especially as the data grows or sources change.