{
  "id": 991,
  "url": "https://arxiv.org/abs/2606.15954v1",
  "title": "Green SARC: Predictive Cost and Carbon Governance for Agentic AI Systems",
  "summary": "Agentic AI systems act through tools and sub-agents, yet the controls meant to bound their financial and environmental cost still sit on dashboards evaluated beside or after execution. Green SARC applies the SARC governance-by-architecture framework -- four enforcement sites in the agent loop -- to FinOps and GreenOps, contributing the theory of what to enforce and how to predict it. We report four policy-independent results. (i) The unconstrained \"State Snowball\" is $Θ(n^2)$ in loop depth; on 3",
  "authors": "Gaston Besanson",
  "category": "research",
  "topics": "regulation,agents-autonomy,environment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-06-14T18:26:43.000Z",
  "fetched_at": "2026-07-14T14:14:54.536Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/991",
  "original_url": "https://arxiv.org/abs/2606.15954v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}