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Autonomous smart grids increasingly rely on AI-driven decisions, yet legal frameworks have not kept pace. Liability for autonomous actions remains ambiguous, threatening both innovation and accountability. No existing conceptual framework systematically links the algorithmic decision layer, operator oversight, and regulatory auditability to address the liability gap in autonomous grid operations. This creates uncertainty when harmful outcomes arise from distributed interactions among software, operators, vendors, and regulated utilities. This article develops a conceptual framework that connects these three layers into a coherent liability-aware governance architecture for autonomous smart grids. The aim is to clarify how responsibility could be assigned, reviewed, and operationalized before and after autonomous grid actions occur. The framework is built through conceptual synthesis of AI law, human-in-the-loop research, and regulatory governance literature. No experiments are conducted, and no quantitative data are presented. The framework defines the roles and interactions of algorithmic decisions, operator oversight, and auditability mechanisms, and derives eight testable propositions. Two tables summarize the decision-liability mapping and the framework logic. The liability-aware framework provides a foundation for designing accountable autonomous grid systems and for future interdisciplinary legal-technical research. It supports legal clarity while preserving the operational benefits of intelligent grid automation.