This topic covers the governance, security and compliance controls organizations need to run agentic AI safely in production. With multi-agent workflows and automated assistants becoming common, enterprises must combine vetted skill libraries, runtime risk scanners, identity and access governance, and provider-level safety features to manage data loss, unwanted actions, and regulatory obligations. Key capabilities fall into four areas: (1) skill and model vetting (e.g., vendor-verified skills and curated skill marketplaces), (2) runtime observability and risk scanning to detect anomalous agent behavior and data exfiltration, (3) identity and entitlements management to enforce least privilege and traceability (SailPoint-like controls), and (4) model and infra choices that support isolation and private models (Cohere-style private LLMs, platform-level assurances such as OpenAI Daybreak). Practical implementations combine platform orchestration (Kore.ai’s multi-agent workflows and governance), infrastructure-level visibility (Xilos’ agentic infrastructure claims of end-to-end visibility), and workspace or domain integrations (Notion for knowledge and automation, specialized agents such as Pria AI for sales workflows). As of 2026, this mix is timely because agentic systems have grown more distributed and regulated: organizations face stronger regulatory expectations for auditability and supply-chain transparency, while vendors add vetted-skill programs and runtime detection to reduce misuse. Effective governance is therefore composable: enforceable policies, continuous risk scanning, identity controls, and use of private/custom models — all integrated into observability and compliance tooling to deliver accountable, auditable agent deployments without sacrificing automation value.