Making Failure Safe: A Constrained, Verifiable Agent Framework for Open-Web Data Collection

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[Submitted on 25 Jun 2026]

Title:Making Failure Safe: A Constrained, Verifiable Agent Framework for Open-Web Data Collection

View a PDF of the paper titled Making Failure Safe: A Constrained, Verifiable Agent Framework for Open-Web Data Collection, by Bo Chen

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Abstract:LLMs and agents can generate web scrapers from natural-language requirements, but direct generation remains unreliable because of dependency errors, broken selectors, schema mismatches, and heterogeneous page structures. We propose a constrained, verifiable agent framework that shifts LLM output from free-form code to typed JSON collector configurations, combining a six-type collector taxonomy, template and utility-function constraints, static Airflow DAG execution, rule-based quality checking, and structured feedback correction. Experiments on 138 tasks show that the taxonomy supports description-based requirement typing, while confirming that stable instantiation requires completing source, field, and execution constraints beyond the initial description. On 80 independently source-verified tasks, the framework runs with zero execution-stage LLM tokens and the lowest average wall-clock time, trading moderate one-shot quality for a reusable, deterministic, and verifiable execution path suited to repeated scheduled collection. These results position the framework as a reusable, low-cost, and verifiable execution path for repeated open-web data collection.

Comments: 15 pages, 1 figure

Subjects:

Artificial Intelligence (cs.AI)

Cite as: arXiv:2607.00035 [cs.AI]

(or arXiv:2607.00035v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2607.00035

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Bo Chen [view email] [v1] Thu, 25 Jun 2026 14:05:37 UTC (22 KB)

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