In an era where 70% of companies employ agentic AI, the challenge lies in managing these autonomous systems to ensure they align with organizational objectives while mitigating risks. AI governance emerges as a crucial framework designed to ensure AI systems operate safely, ethically, and in accordance with legal and strategic goals, incorporating policies, technical controls, and accountability measures. Unlike traditional data governance, AI governance must manage the unpredictability of agents that learn and adapt, necessitating real-time observability tools and layered safeguards such as alignment tests and human override mechanisms. Effective AI governance reduces production incidents, accelerates regulatory compliance, detects early drift, and minimizes technical debt, thus providing a competitive edge in regulated industries. Governance should be integrated into every phase of the AI agent lifecycle, from design and architecture to deployment and monitoring, with clear ownership roles and federated structures ensuring accountability. Overcoming common challenges like balancing innovation speed with compliance and managing bias requires embedding governance controls into pipelines and adopting continuous monitoring platforms. Tools like Galileo offer solutions by providing real-time decision lineage, conflict monitoring, and automated compliance scorecards, turning AI systems into reliable strategic assets.