This topic covers platforms and frameworks that govern, secure, and operationalize AI agents in enterprises — from recommendation engines and no-code agent builders to regulatory-compliance tooling and safety controls. As organizations deploy multi-agent orchestrations and domain-specific assistants, governance tooling centralizes policy enforcement, audit trails, access controls, and incident response to reduce operational risk and meet growing regulatory expectations. Relevance in late‑2025/early‑2026 is driven by two converging trends: widescale enterprise adoption of AI agents (productivity copilots, contact‑center voice agents, legal and professional‑service assistants) and intensifying oversight from regulators and auditors. Practitioners now need toolchains that provide transparent model provenance, data lineage, human‑in‑the‑loop controls, runtime guardrails, and automated compliance reporting. Key tools and categories represented here include enterprise agent builders and orchestrators (IBM watsonx Assistant and Cimba.AI — no‑code and developer workflows for building, training, and deploying auditable agents); embedded productivity copilots (Microsoft 365 Copilot — AI assistance integrated into office apps requiring enterprise data governance); contact‑center and voice AI platforms (Observe.AI — real‑time assist, VoiceAI agents, and QA auditability); domain‑specific assistants (Harvey — legal/professional workflows with knowledge and workflow controls); and base conversational/developer models (Anthropic’s Claude family) used as underlying engines. Evaluations in these rankings emphasize measurable governance capabilities: policy enforcement, explainability and logging, role‑based access, lineage and evidence for decisions, integration with security stacks, and support for sectoral compliance. The goal is pragmatic: assess how platforms balance agent utility with safety, traceability, and regulatory readiness for enterprise deployment.