AI Governance & Compliance Tools for Creative and Enterprise Use covers the platforms and practices organizations use to make agentic AI safe, auditable, and compliant across customer-facing and internal workflows. As enterprises deploy voice and chat agents, multi-agent orchestrations, and embedded copilots, governance must span model inventories, policy-as-code, data lineage, runtime controls, and human‑in‑loop checkpoints. The result is a stack that combines conversation-intelligence, orchestration, observability, and knowledge management. Key vendor types include contact‑center conversation-intelligence platforms (e.g., Observe.AI) that provide VoiceAI, real-time assist and automated QA; end-to-end no-code/low-code platforms (e.g., StackAI) that let teams build, deploy and govern enterprise agents; multi-agent orchestration platforms (e.g., Kore.ai) with built‑in observability and workflow governance; enterprise assistant frameworks (e.g., IBM watsonx Assistant) for no-code and developer-driven automation; agentic CX/EX platforms (e.g., Yellow.ai) for cross-channel autonomous agents; and knowledge/workspace tools (e.g., Notion) used to codify policies, runbooks and evidence for audits. This topic is timely because widespread adoption of agentic AI has increased regulatory and operational risk: organizations need enforceable controls for data privacy, model behavior, and explainability while maintaining productivity. Current best practices emphasize integrated observability, continuous monitoring and red‑teaming, role-based access and model/version controls, and treating governance as part of the development lifecycle. Evaluating tools means mapping requirements (contact center compliance, enterprise automation, security posture) to capabilities such as real‑time policy enforcement, audit trails, and low‑code governance to scale controls without blocking innovation.