This topic covers enterprise software and services that centralize AI governance, regulatory compliance, and model‑risk management across production models and agentic workflows. Growing regulatory scrutiny, industry guidance, and widespread deployment of generative and agentic systems have made continuous monitoring, vendor governance, validation, and policy enforcement core operational needs for enterprises as of 2026. Key categories include AI governance platforms (policy lifecycle, model inventory, explainability and validation), regulatory compliance tools (evidence collection, reporting, audit trails), and AI security/governance (access controls, observability, anomaly detection). Representative offerings illustrate common approaches: Monitaur targets insurance and other highly regulated industries with centralized policy management, monitoring, validation and vendor governance; Kore.ai and Yellow.ai focus on building and orchestrating multi‑agent workflows while embedding observability and governance; StackAI emphasizes no‑code/low‑code agent lifecycle and governance for enterprise automation; Observe.AI delivers conversation intelligence and real‑time compliance/QA for contact centers; Microsoft 365 Copilot demonstrates embedding assistant capabilities across productivity apps, creating a new surface for governance and data controls. Trends reflected across these tools include policy‑as‑code, real‑time model and agent observability, automated validation and QA, vendor/supply‑chain oversight, and low‑code interfaces to lower friction for compliance. Enterprise programs now combine technical controls (monitoring, drift detection, access governance) with operational processes (risk scoring, audit evidence, cross‑vendor orchestration). For procurement and security teams, evaluating platforms means balancing domain specialization (e.g., contact centers, insurance) with enterprise needs for interoperability, auditability and centralized model‑risk workflows.