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

• URL: http://arxiv.org/abs/2607.00035v1
• Date: Thu, 25 Jun 2026 14:05:37 GMT
• Title: Making Failure Safe: A Constrained, Verifiable Agent Framework for Open-Web Data Collection
• Abstract summary: LLMs and agents can generate web scrapers from natural-language requirements, but direct generation remains unreliable.<n>We propose a constrained, verifiable agent framework that shifts LLM output from free-form code to typed collector configurations.
• Score: 4.093009863847175
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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