What is Multi-Agent Systems?

In practice, multi-agent systems may include multiple models, autonomous tools, orchestration layers, or human-in-the-loop components that each perform part of a workflow. The compliance challenge is that failures can emerge from the interaction between agents even when each individual component appears acceptable on its own, so teams need to document roles, interfaces, control points, escalation paths, and override mechanisms.

This matters for governance because regulators and assurance frameworks increasingly expect organizations to understand how AI systems behave in deployment, not just how a single model performs in testing. The concept is relevant to AI governance programs under ISO/IEC 42001, NIST AI RMF, and EU AI Act-oriented compliance work, especially where system-level risk assessment, logging, human oversight, and change management are required or recommended. It is also closely linked to cybersecurity and third-party risk controls when agents call external tools, APIs, or services.