Single-model chatbots were the easy case. Multi-agent systems — planners, tool callers, retrievers, and orchestrators chaining actions across APIs — multiply risk faster than most governance programs evolved.
Why agents change the equation
Traditional ML governance focused on training data, model cards, and batch inference. Agents add autonomy: they select tools, chain requests, and act on live systems. A misconfigured agent can exfiltrate HR data, trigger production changes, or send customer communications — without a human clicking approve on each step.
Three control layers
Access control limits which tools, environments, and data paths an agent can reach. Decision control applies policy before consequential actions — approvals, elicitations, and risk-tier gates. Execution control enforces allow/deny at runtime when the agent actually invokes a tool.
Skipping any layer leaves a hole. Approving a use case in a committee does not stop an agent from calling an undeclared API at 2 a.m.
Governance across the lifecycle
- Deploy gate — block staging/production until intake documents tools and boundaries
- Assessment — risk tier, controls, and draft runtime policy from structured intake
- Evidence — compliance tests prove controls work before audit sign-off
- Runtime — policy manifest enforced on every tool call with full audit trail
Shared workflow, two teams
Engineering owns build and deploy; GRC owns review and audit. Both need purpose-built consoles — not shared tickets and Slack threads. openRegal connects them with handoffs at each stage so multi-agent governance scales beyond the first pilot.