Green SARC: Predictive Cost and Carbon Governance for Agentic AI Systems
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
Record details
Published: 14 June 2026
Source: arXiv
Category: Research
Topics: Regulation · Agents & autonomy · Environment
Retrieved: 14 July 2026
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ethics.ai (14 June 2026), “Green SARC: Predictive Cost and Carbon Governance for Agentic AI Systems,” evidence record 991, https://ethics.ai/record/991 (originally published by arXiv).
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