Agent-to-agent governance is the control of context passed between AI agents — constraining what one agent may delegate or disclose to another, preserving the originating identity and purpose across the exchange, and recording the chain for audit.
What is agent-to-agent governance?
Multi-agent systems create a path around every control applied at the edge. A governed agent can retrieve permitted context and pass it to a second agent operating under different entitlements, for a different purpose, on behalf of a different user — and nothing in a per-agent control model examines that handover. Governing the exchange means treating each message as a governed transfer: policy is re-evaluated at the boundary, identity and purpose propagate rather than reset, and the delegation chain is reconstructable afterwards.