arXiv:2607.00035v1 Announce Type: new 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 1
The rapid advancement and adoption of LLMs as primary tools for automation are highlighting the current reliability issues in agentic web data collection, demanding more robust and verifiable frameworks.
This development addresses a critical bottleneck in the practical application of AI agents for data collection, moving them from unreliable code generation to verifiable configurations, thereby enhancing their utility in business and intelligence.
The shift from free-form code to constrained, verifiable JSON configurations for web scrapers significantly improves the reliability and maintainability of AI-generated data collection agents.
- · AI agent developers
- · Data intelligence platforms
- · Businesses relying on web data
- · NLP researchers
- · Manual web scraping services
- · Companies with unreliable data pipelines
More reliable and scalable web data collection for AI agents becomes possible.
Increased adoption of AI agents for complex, real-world data gathering tasks across various industries.
The development of a standardized, verifiable language for agentic web interaction, becoming a foundational layer for multi-agent systems.
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Read at arXiv cs.AI