{
  "id": 6465,
  "url": "https://arxiv.org/abs/2604.01647v1",
  "title": "Exploring Robust Multi-Agent Workflows for Environmental Data Management",
  "summary": "Embedding LLM-driven agents into environmental FAIR data management is compelling - they can externalize operational knowledge and scale curation across heterogeneous data and evolving conventions. However, replacing deterministic components with probabilistic workflows changes the failure mode: LLM pipelines may generate plausible but incorrect outputs that pass superficial checks and propagate into irreversible actions such as DOI minting and public release. We introduce EnviSmart, a productio",
  "authors": "Boyuan Guan, Jason Liu, Yanzhao Wu, Kiavash Bahreini",
  "category": "research",
  "topics": "agents-autonomy,environment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-04-02T05:46:40.000Z",
  "fetched_at": "2026-07-14T16:32:33.098Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/6465",
  "original_url": "https://arxiv.org/abs/2604.01647v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}