AI agent management is crucial for ensuring the secure and reliable operation of AI agents across enterprise software, which are anticipated to become more prevalent by 2028. This involves implementing proactive and reactive measures, such as enforcing governance rules, monitoring performance, and addressing issues via AI agent management platforms. These platforms allow organizations to integrate AI agents with tools from Model Context Protocol servers, monitor tool calls, and manage customizable rules to control how agents interact with data. Effective management helps prevent data leaks, ensures agents use the right tools, and provides visibility on potential issues during testing. The document highlights several use cases, including lead routing and incident management, illustrating how AI agents can be managed to perform specific tasks while adhering to organizational policies. Best practices include organizing connectors and tools into collections that map to business use cases and adopting a comprehensive platform like Merge Agent Handler, which provides prebuilt connectors, policy-based rules, and evaluation suites to maintain security and scalability.