Autonomous AI agents can plan, call tools, communicate with other systems, and execute operational actions without continuous human direction. These capabilities create a governance problem that differs from conventional model oversight because harmful effects may arise from sequences of actions, changing environments, and interactions across organizational boundaries. This study develops and empirically evaluates the Human Oversight Governance Framework for Autonomous AI Agents (HOGF-AI). The evidence base is a structured content analysis of 24 public governance instruments available by May 31, 2025, with six documents each from healthcare, finance, energy, and public infrastructure. A 24-item codebook operationalizes eight dimensions: intervention authority, escalation and accountability, monitoring and validation, explainability and traceability, risk and cybersecurity, fairness and contestability, lifecycle control, and organizational learning. Each provision was scored on a five-level operationalization scale. Internal consistency was high across all dimensions, with Cronbach's alpha from 0.842 to 0.972. An exploratory two-component partial least squares model explained 79.0 percent of leave-one-out variation in operational Responsible AI readiness. Monitoring and validation, lifecycle and change control, and escalation and accountability had positive bootstrap-supported coefficients. The equal-weight Human Oversight Governance Index correlated with readiness at Spearman rho = 0.643. Public infrastructure and healthcare scored highest overall, while energy guidance was strongest in safety and cybersecurity but weaker in rights, intervention, and learning. The findings show that meaningful oversight is not a single human approval step. It is a lifecycle capability that combines bounded autonomy, evidence-based escalation, stop authority, continuous validation, audit records, and institutional learning. The framework and index provide a reproducible benchmark for organizations deploying autonomous agents in high-stakes settings.