The adoption of multi-agent AI systems is transforming governance and compliance, but it also introduces risks that conventional monitoring cannot detect. Autonomous agents communicate, delegate tasks, and chain reasoning in ways that create invisible failure modes and policy violations.

AI control planes act as a guardian layer for these systems, combining observability, guardrails, and governance into a unified framework. Beyond passive monitoring, they analyze context in real time, intercept unsafe actions, dynamically rerouting agent workflows, and enforcing policy compliance across agent interactions. This proactive approach allows enterprises to prevent violations before they escalate.

In this session, we will explore how a control plane for multi-agent AI works in practice, demonstrating scenarios where real-time guardianship enables safe and compliant multi-agent AI at scale.