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

Description and use

Autonomous agents can build fully functional web‑scraping pipelines in minutes, yet without proper tooling, clear contracts, and regular review, data quality suffers.

Problem

Developing production‑grade web‑scraping pipelines with AI agents required more than just prompting; it demanded specialized tooling, clear contracts, and rigorous review loops.

Outcome

Agents can build complete, tested scraping pipelines in under an hour when supported by tailored tools and clear contracts, but speed alone does not guarantee quality; continuous review and schema enforcement are essential.

Workflow

• 1. Define target URL and project structure
• 2. Build CLI wrappers to shape tool responses
• 3. Provide agent with structured context and domain skills
• 4. Agent generates pipeline code, selectors, and tests
• 5. Review agent output for correctness
• 6. Deploy and monitor spider
• 7. Iterate with schema validation and automated checks

Evidence

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

Source

I ended up constructing a set of CLI wrappers around my most‑used services – primarily Zyte API – because the raw responses were not shaped for an agent to reason with efficiently

Source

After day four, I started treating typed item definitions, schema validation, fixture‑based tests, and clear module boundaries as prerequisites rather than nice‑to‑haves

Source