Audit AI agent actions with action-level logging that records what the agent was asked, what it decided, which tools and systems it touched, which credential it used, what it proposed, who approved it, and what changed. This produces one traceable record per business action rather than fragments scattered across systems. Mapping those logs to the NIST AI RMF or a MITRE ATLAS technique supports both incident response and compliance. Logging that captures decisions and credential use, not just model outputs, is what makes an agent's behavior defensible after an incident.

Model Context Protocol is usable in the enterprise, but tool discovery must be treated as untrusted ingress. A tool's description, and any later change to it, can carry instructions the model will follow, so tools cannot be trusted on name alone. Public U.S. National Security Agency guidance on MCP recommends inspecting every tool schema before it reaches the model. In practice, run that inspection and exposure management inside a security control plane, pin and review tool definitions, and give agents least-privilege access to only the tools a task requires.

The EU AI Act applies to AI agents through its risk-based obligations, which depend on how the agent is used rather than on the agent label. Obligations for general-purpose AI models applied from August 2, 2025, and penalties reach up to 35 million euros or 7% of global turnover for prohibited practices. The Digital Omnibus on AI, formally adopted by the European Parliament on June 16 and the Council on June 29, 2026, fixed the revised high-risk dates: December 2, 2027 for standalone Annex III systems and August 2, 2028 for Annex I embedded systems. Map your agent use cases to the Act's [...]

Audit AI agent actions with action-level logging that records what the agent was asked, what it decided, which data and systems it touched, what it proposed, who approved it, and what changed. This produces one traceable record per business action rather than fragments scattered across systems. Regulatory mapping then links those logs to the relevant framework, such as the NIST AI RMF or an ISO/IEC 42001 control, so the audit trail supports both internal review and external compliance. Logging that captures decisions, not just outputs, is what makes agent behavior defensible.

An iPaaS such as Boomi or MuleSoft moves data reliably but does not enforce agent-level policy, authentication, and action logging on its own. A governance control plane is designed to sit above the iPaaS, loosely coupled and platform-agnostic, and intercept agent actions before they reach enterprise systems. For read-only, single-system agents, native platform controls may be enough. For agents that act across systems or carry financial and regulatory consequence, the control plane adds the zero-trust authentication, dynamic policy gates, and kill switch that an integration platform was not built to provide.

Use them together rather than choosing one. The NIST AI Risk Management Framework gives a risk operating model, ISO/IEC 42001 provides a certifiable management system and third-party audit signal, the EU AI Act sets binding obligations if you operate in or serve the EU, and OWASP and MITRE ATLAS list specific agent threats to control. None was written for autonomous agents, so extend them with an enforcement layer that applies policy at the point of action. The frameworks define duties; the control plane enforces them.

AI governance manages models and their outputs, while AI agent governance manages what autonomous agents do across enterprise systems. Traditional AI governance covers model risk, bias, and content safety. Agent governance adds authentication, authorization, real-time policy enforcement, human approval, and action-level audit, because an agent can read, write, and transact rather than only generate text. The distinction matters as agents spread: Gartner projects 40% of enterprise applications will embed task-specific AI agents by the end of 2026, up from less than 5% in 2025.