AI Governance, Monitoring and Compliance for Autonomous Agents covers the policies, technical controls and operational tooling organizations need to safely deploy agentic AI that acts on business workflows, customer conversations and automated decision-making. By 2026, increased enterprise adoption of multi-agent orchestrations, voice-first interfaces and continual model updates has made runtime observability, provenance tracking, access controls and auditability core compliance requirements. Key classes of tools include platform-level governance (no-code/low-code orchestration and policy enforcement), runtime monitoring and conversation intelligence, model/MLops platforms for lifecycle controls, and domain-specific agent providers. Examples: StackAI provides end-to-end no-code/low-code capabilities for building, deploying and governing enterprise agents; Vertex AI offers managed model lifecycle, deployment and monitoring services on cloud; IBM watsonx Assistant supports enterprise virtual assistants with multi-agent orchestration and governance hooks; Observe.AI and PolyAI focus on contact-center voice and conversational monitoring, with auto-QA, real-time assist and records that support compliance and quality controls; Yellow.ai targets agentic CX/EX automation with multi-channel governance features. Practical governance priorities include recording and indexing agent interactions for audit trails, data lineage and consent management for PII, runtime safety guards and escalation paths, continuous evaluation (synthetic and live), role-based access and change-control workflows, and mechanisms for third-party model/tool risk assessments. As regulation and standards around automated decision-making and voice services solidify, organizations should combine platform-native controls, conversation-intelligence layers and cloud MLops features to meet security, privacy and regulatory obligations while maintaining operational visibility and incident preparedness.