I built scraping agents for 30 days - here’s what I learned

The autonomous web data pipeline is dawning. Going deep on the frontier’s state of the art surprised me, humbled me, and changed how I work.

The industry is abuzz with talk about autonomous web data-gathering agents. But what’s the reality?

For the last 30 days, I did one thing almost exclusively: I built scraping systems with AI agents, from the ground up, across real targets, with real deadlines. Not prototypes designed to impress in a demo, not isolated experiments running against a toy website, but production-grade pipelines that needed to ship and keep running.

I went in with genuine curiosity and came out with something more useful than a hot take: a messy, hard-won picture of where agents genuinely changed my work, where they burned my time, and what I had to unlearn about how I thought development with AI would feel.

The biggest surprise: Agents really can build pipelines

I did not go into this expecting agents to handle the full build cycle. My assumption, going in, was that they would be useful for the boring middle parts: generating boilerplate, suggesting selectors, maybe scaffolding a spider I would then finish myself.

What I found instead was that, with the right tools and domain-specific context in place, an agent could take a target URL and produce a working, tested pipeline without me writing the bulk of the code. Not just a rough scaffold that got me 60% of the way there - a working pipeline, with selectors, page objects, item definitions, and a passing test suite.

The first time this happened cleanly I actually went back and I checked the output, because I did not quite believe it. The spider ran, the items validated, and the tests passed.

I had been expecting to spend the entire afternoon on it, but it took 45 minutes, and most of that was me reviewing the output rather than the agent producing it. That shift, from writing to reviewing, is the one I keep coming back to when I think about what actually changed over these 30 days.

The caveat I discovered quickly, though, is that the words "right tools" are doing enormous work in that sentence.

The first few sessions where I pointed a general-purpose agent at a blank project and a URL were humbling, with hallucinated selectors, invented conventions, and code that looked plausible - until it completely fell apart on real pages.

It was only once I built out the proper scaffolding around the agent, a structured project to conform to, Zyte-specific skills and tooling, and well-defined context about how we build at Zyte, that the results became something I could trust.

Zyte's own research into what makes agents succeed or fail at web scraping tracks closely with what I experienced: specialized tooling and context engineering are what separate useful agents from expensive chaos.

The biggest time sink: Tool design

I did not anticipate that the biggest investment of the 30 days would be in building tools rather than in prompting or orchestration, but that is exactly what happened.

I ended up constructing a set of CLI wrappers around my most-used services - primarily Zyte API and related infrastructure - because the raw responses were not shaped for an agent to reason with efficiently, and I kept watching agents get confused by the volume and structure of what came back.

Each tool I built was designed so that the data coming back to the agent was precisely what it needed for the task and nothing else: no raw HTML dumps, no verbose JSON payloads with 40 fields when three were relevant, no response structures that required the agent to do significant parsing just to understand what it had received.

The difference this made was not subtle. Before those tools, I was watching agents latch onto the wrong signal and carry that error confidently through the rest of their work. After, the reasoning was noticeably cleaner and the output was dramatically more reliable.

What I came to understand is that the bottleneck was never the model, it was the quality of the interface between the agent and its tools, and I had been underinvesting in that interface completely. The work of building good tools felt like plumbing at the time, but, looking back, it was the most valuable engineering I did across the entire 30 days.