arXiv:2606.15954v1 Announce Type: cross Abstract: 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 $\Theta(n^2)$ in loop depth; on 3,000 real multi-step plans (SWE-rebench) it h
As agentic AI systems become more sophisticated and widely deployed, the immediate need for robust cost and carbon governance mechanisms is emerging alongside deployment. Current solutions are reactive, making predictive control a critical next step.
This development addresses a fundamental challenge for the scalable and sustainable deployment of autonomous AI, moving from reactive monitoring to predictive governance of financial and environmental costs. It directly impacts the economic viability and environmental footprint of advanced AI applications.
The introduction of predictive governance frameworks like Green SARC allows for proactive management of resource consumption and carbon emissions in agentic AI, enabling more efficient and responsible system design and operation. It shifts control from post-execution dashboards to in-loop enforcement sites.
- · AI platform providers
- · Cloud infrastructure providers
- · Enterprises adopting AI agents
- · Sustainability tech companies
- · Inefficient AI system developers
- · Companies with high energy costs
- · Organizations without FinOps/GreenOps strategies
Widespread adoption of predictive FinOps and GreenOps for agentic AI systems will lead to optimized resource allocation and reduced operational costs.
This optimization will accelerate the deployment of complex, multi-agent AI systems in sensitive sectors, contingent on meeting specific cost and carbon targets.
The ability to precisely control the economic and environmental impact of AI agents could become a competitive advantage, leading to a new class of 'efficient AI' products and services.
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Read at arXiv cs.AI