Prove7 Control Vector™ performs real-time AI governance on every agent call. Policy is enforced inline — at agent entry, model inference, and connector-out — and autonomy is revoked the instant an agent's trust score slips. This is runtime agent control: permit, shape, or stop an agent while it runs.
Real-time AI governance is the enforcement of policy at the exact moment an AI agent acts — rather than reviewing logs after the fact. Instead of writing a policy and hoping the agent obeys it, Prove7 evaluates every governed call inline and decides, in the path of execution, whether to permit, warn, or block it. Governance lives with the action, not beside it.
This is the difference between observability and control. Logging tells you what an agent already did. Runtime agent control lets you stop it before it does the wrong thing — and expand its autonomy only as far as its measured trust allows.
Prove7's Governed Execution Layer wraps every consequential action. Each call passes the same governance chain, in the path of execution:
The acting agent presents its own cryptographic identity — never a shared key.
The action is checked against the agent's declared intent, not just its role — and recorded as a verdict.
Granted scope and connector assignments are enforced at execution, not merely configured.
Controls evaluate at agent entry, model inference, and connector-out — in observe (shadow) or hard-block mode, per control.
The run is scored; sustained conformance promotes autonomy, drift auto-probates the agent in real time.
The action and every verdict are sealed to the hash-chained, per-tenant audit trail.
Permit, warn, or block any governed call at three enforcement points — as an MCP gateway, SDK decorator, or native gate.
A live Agent Trust Score raises autonomy as trust is earned and lowers it automatically when trust slips — no manual intervention.
Consequential actions pause for a human decision that no configuration can silently remove.
Halt an agent — or an entire class of agents — immediately, and resume the deterministic path.
Roll out any control in shadow mode first: it evaluates and seals every verdict without blocking, so you see impact before you enforce.
The deterministic path is never deleted; it is the floor the system falls back to. See the Trust Transfer Framework.
Real-time AI governance is the enforcement of policy at the exact moment an AI agent acts — at agent entry, model inference, and connector-out — in observe or block mode, rather than reviewing logs after the fact. Prove7 Control Vector™ evaluates every governed call inline and can permit, warn, or block the action, and revoke the agent's autonomy the instant its trust score drops.
Runtime agent control is the ability to permit, shape, or stop an AI agent's actions while it runs — not just at design time. Prove7 provides it through a Governed Execution Layer that gates every agent call, a live trust score that raises or lowers autonomy automatically, human approval gates on consequential actions, and a kill switch that halts an agent immediately.
Guardrails typically filter model inputs and outputs for unsafe content. Real-time AI governance governs the agent's actions and identity — authorizing each call against declared intent and least-privilege access, scoring trust continuously, enforcing policy inline, and sealing every decision to a tamper-evident audit trail. Prove7 does both, but governs the whole action, not just the text.
At three runtime enforcement points on every governed call: agent entry (is this agent allowed to act), model inference (is this prompt and response within policy), and connector-out (is this outbound action authorized). Enforcement runs as an MCP gateway, an SDK decorator, or native gates, in observe-only or hard-block mode per control.
See Prove7 Control Vector™ enforce policy inline, score trust, and stop an agent mid-run — in 30 minutes.
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