Your AI Agents. Governed and Defended.
AI agents operate at machine speed: decisions in milliseconds, actions chained across systems, no human in the loop. Our team configures authority boundaries, circuit breakers, and decision chain verification for every agent. We monitor them continuously and intervene when they exceed their mandate.
VALID Layer D is the part of our framework that governs agentic AI. Our specialists translate it into live controls: authority scopes, circuit-breaker thresholds, decision-log verification, for every agent in your portfolio.
Why Traditional Governance Fails for Agents
AI agents operate differently from traditional AI systems. They require fundamentally different governance controls.
Speed
Agents make thousands of decisions per minute. Human review of every action is impossible. You need automated governance that operates at agent speed.
Chaining
Agents chain actions across systems: one decision triggers the next. A single misconfigured permission can cascade through your entire infrastructure.
Autonomy
Agents operate without direct human oversight by design. Without authority boundaries and circuit breakers, there is no safety net when things go wrong.
How Agentic Defence Works
Five integrated capabilities that govern AI agents from deployment through every decision they make.
Authority Boundary Management
Define and enforce what each AI agent can and cannot do in real time.
Integrated Monitoring
Continuous assurance that all agentic controls are functioning. D-05 ties together D-01 through D-04 with 5 sub-requirements evaluated automatically.
Authority Boundary Validation
Verify all agents operate within their configured permission scopes. Flag any boundary drift.
Circuit Breaker Health
Confirm all circuit breaker conditions are active and responding within latency thresholds.
Decision Log Integrity
Validate hash chain integrity across all agent decision logs. Alert on any broken chains.
Shadow AI Scan Status
Verify shadow AI detection scans are running on schedule. Report any new unregistered systems.
Maturity Score Calculation
Aggregate sub-requirement scores into the overall D-05 maturity level (0–5).
Scenario: Refund Agent
See how agentic defence controls work together in a real-world scenario. A customer requests a refund that exceeds the agent's authority boundary.
Customer submits refund request via chat
Receives request, begins processing
Agent looks up order history
Retrieves order #4821, £2,340 value
Amount exceeds authority boundary
D-02 triggered: spend threshold (£500 limit)
Action Level 4: Pause
Agent paused. Human approval required.
Alert sent to finance team
Slack notification + email with full decision chain
Finance approves partial refund
Approves £1,170 (50%) with reason code
Agent resumes with override
Processes £1,170 refund. Full chain logged.
Decision log entry created
Hash chain updated. Audit trail complete.
Book a Scoping Call
Tell us about your AI agents. We will scope the governance in 30 minutes.