Watts and Debts of Agentic Frameworks: An Empirical Study (Registered Report)

• URL: http://arxiv.org/abs/2606.10702v1
• Date: Tue, 09 Jun 2026 11:03:20 GMT
• Title: Watts and Debts of Agentic Frameworks: An Empirical Study (Registered Report)
• Abstract summary: Self-Admitted Technical Debt (SATD) and hardware-level runtime energy consumption will be studied.<n>Findings will establish whether automated source code analysis can serve as a reliable, early-warning proxy for energy-efficient framework selection.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Context: Every agentic AI system shipped to production carries two hidden risks: accumulated Technical Debt (TD) and unmonitored runtime energy costs. While functional benchmarking is common, the empirical link between internal structural quality (specifically TD) and dynamic energy consumption during execution remains unexplored, creating a blind spot for practitioners and organizations managing sustainability and operational budgets at scale. Goal: We propose a confirmatory empirical study correlating Self-Admitted Technical Debt (SATD) with hardware-level runtime energy consumption across agentic frameworks, to determine whether code quality can drive energy-aware design decisions. Method: We will evaluate five open-source agentic frameworks by executing a standardized task suite in a strictly controlled environment. SATD will be extracted via automated Python-based comment mining and categorized via LLM-based classification using fine-tuned prompt, while runtime energy will be measured at the hardware level. Our study will investigate three core research questions: (RQ1) the presence of TD within these frameworks; (RQ2) the variance in runtime energy consumption across architectures; and (RQ3) the statistical correlation between a framework's TD and its task-level energy consumption. Conclusion: The findings will establish whether automated source code analysis can serve as a reliable, early-warning proxy for energy-efficient framework selection, thereby advancing both green software engineering and agentic AI quality research.

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