AI leaders face significant challenges in trusting and governing autonomous agents, with 57% lacking confidence in their outputs and 60% unable to explain how these agents handle sensitive data. Only a small fraction of enterprises have mature AI governance frameworks, which is concerning given the critical roles these agents play in workflows. This situation is exacerbated by the speed at which autonomous agents operate, making traditional governance methods inadequate. The proposed 10-step framework aims to address these issues by starting with lightweight pilots, integrating measurable guardrails, and automating oversight to maintain innovation while ensuring risk management. The framework emphasizes the importance of cross-functional collaboration, mapping agent lifecycles, establishing clear policies, deep observability, continuous evaluation, runtime protection, and audit-ready documentation. It also highlights the need for operational review loops and scalable governance through automation and continuous learning. This structured approach aims to close the governance gap, satisfy risk teams, and maintain executive confidence without stifling innovation.