What an agent can determine from a business website

Hypothesis

Agents will extract entities and services accurately from semantically structured sites and fail in predictable ways on sites where content lives in scripts, images or hover states.

Method

1. Select a small sample of real business websites across categories, including this one.
2. Run the same task battery against each with a browser agent: identify the legal entity, list services, find pricing or engagement terms, locate a contact action and describe how to complete it.
3. Score outputs against ground truth established by manual review.
4. Record the failure type when extraction fails: missing content, ambiguous labels, inaccessible controls, or hallucinated filler.

Environment: Browser agent over live public sites; manual ground-truth review

Variables: Site structure (semantic vs script-rendered); Label explicitness; Action design

Result and current observation

No result yet. Baseline task battery defined and first runs scheduled; observations will be published here with the scoring sheet.

Limitations

• Small sample; results describe failure patterns, not category-level rates.
• Agent behavior changes with model versions; runs are dated and versioned.

Replication notes

Replicable with any browser agent: fix the task battery before looking at any site, score against manually established ground truth, and date the model version used.