AI agent governance
An agent can have access to the right tool and still execute the wrong thing. API permission does not prove that enough evidence, human authority or context exists to create that consequence.
AI agent governance
Agentic Consequence Control Plane
Sits between an AI agent and the real world to decide whether an action may run, needs approval or must be blocked.
01 / In 20 seconds
Sits between an AI agent and the real world to decide whether an action may run, needs approval or must be blocked.
An agent can have access to the right tool and still execute the wrong thing. API permission does not prove that enough evidence, human authority or context exists to create that consequence.
What needs to change
Autonomy stops meaning “the agent has access” and becomes “the agent may act within a governed trajectory”.
What we seek
More agent autonomy without giving up control, authority, traceability or the ability to veto.
What exists today
Executable product with a pilot path. Value and coverage depend on integration with the tools, policies and authorities of the target environment.
02 / Before and after
The difference appears in the next decision.
Without Agentic Consequence Control Plane
An agent can have access to the right tool and still execute the wrong thing. API permission does not prove that enough evidence, human authority or context exists to create that consequence.
With Agentic Consequence Control Plane
- Receives the goal, scope, state, tools, credentials and applicable policies.
- Evaluates the proposed trajectory, available evidence, risks and required authority.
- Allows, conditions or vetoes the action before it creates a real consequence.
- Verifies the result and records what the agent actually changed.
03 / What goes in
The product needs the parts of reality that can change the decision.
Agent objectiveThe change the agent was authorized to pursue.
ToolsAPIs, systems and actions the agent may attempt to use.
CredentialsWhich accesses exist and which limits accompany each one.
Current stateData and context required to evaluate the proposed trajectory.
PoliciesBusiness, security, privacy and operational rules.
ApprovalsWhen an action requires human authority or additional confirmation.
Input format can vary by client. Integration is a means; information quality and origin remain explicit.
04 / What Oruvo does
Oruvo turns fragmented input into a governed next move.
Step 1
Receives the goal, scope, state, tools, credentials and applicable policies.
Step 2
Evaluates the proposed trajectory, available evidence, risks and required authority.
Step 3
Allows, conditions or vetoes the action before it creates a real consequence.
Step 4
Verifies the result and records what the agent actually changed.
05 / What you receive
The deliverable must be usable by operations, not merely readable.
More agent autonomy without giving up control, authority, traceability or the ability to veto.
Scope boundaries
Tool and credential control
Trajectory checks
Pre-action veto
Human approval
Execution verification
06 / Decision example
How the product changes a decision in practice.
HYPOTHETICAL EXAMPLE — NO CLIENT DATA
An agent is asked to resolve a complaint. It may reply to the customer and update the CRM, but attempts a refund above its authorized limit. The Control Plane lets admissible actions proceed and stops the financial consequence until valid approval exists.
Autonomy stops meaning “the agent has access” and becomes “the agent may act within a governed trajectory”.
07 / Where outcome appears
Value appears when the decision changes a real consequence.
More agent autonomy without giving up control, authority, traceability or the ability to veto.
Useful automationMore tasks can be delegated without losing material limits.
Operational riskIrreversible or out-of-scope actions can be stopped before consequence.
AuditabilityIt is clear what the agent tried, what was allowed and what actually changed.
08 / How to start
It does not need to start big. It needs to start verifiable.
Start with a real slice
We define the objective, minimum data, the decision that must change and the outcome criterion. Then we build the smallest pilot able to prove or disprove value.
Current maturity
Executable product with a pilot path. Value and coverage depend on integration with the tools, policies and authorities of the target environment.
Talk to Oruvo →Limits that remain
- The agent does not self-authorize.
- Tool access does not prove an action is admissible.
- Execution is not success until the outcome is verified.