Enterprises can now build AI agents that act. Almost none can prove, to an auditor, what an agent did and why it was allowed to. Prove7 Control Vector™ closes that gap — governing every AI agent in real time through seven trust gates, so you can run agents in production and keep the proof.
Agentic AI governance is the discipline of controlling and proving what autonomous AI agents do at runtime — not in a policy document, but inline on every action. It gives each agent a verifiable identity, authorizes its actions against a declared intent and policy, measures its trustworthiness continuously, and seals every step to a tamper-evident record. Prove7 Control Vector™ delivers agentic AI governance as an Agentic Trust Infrastructure: a runtime layer that measures, enforces, and proves trust for AI agents across any framework, cloud, or runtime.
The reason enterprise AI stalls at the pilot is not capability — it is accountability. Copilots suggest but do not act. Agent frameworks act but cannot prove they should have. Legacy governance writes policy and hopes. Prove7 governs at the point of action, so autonomy becomes something you can measure, defend, and reverse.
Real-time AI governance in Prove7 is structured as seven sequential gates. Agents built on Prove7 inherit all seven by construction; external and third-party agents must pass through every gate to reach the governed core.
Every agent, workflow, and tool inventoried and made known — no shadow AI.
A cryptographic, federated identity per agent. The agent is someone, not something.
Purpose and scope bound to an approved intent; drift from intent becomes detectable.
Role, scope, and entitlement resolved across org, tenant, and instance — least privilege.
Build · Decide · Act. Autonomy earned through governed promotion, not assumed.
Trust-weighted action permitted or blocked at every call — policy inline.
Hash-chained, tamper-evident, attestable. Every decision, every gate — sealed.
The runtime that enforces all seven gates on every governed call.
These are not features bolted onto an agent framework. They are the substrate every agent executes on — which is why a Prove7-governed agent can touch production systems and still be provable.
Every consequential action — a model inference, a connector call, an agent-to-agent hand-off — passes an enforcement point before it happens. This is runtime agent control: governance inline on every call, not policy written and hoped for.
Each agent is issued its own SPIFFE identity from a platform certificate authority — never a shared API key. Every governed call carries a machine identity, so "which agent did this, under what authority" is always answerable. This is the foundation of non-human identity governance for AI agents.
Prove7 authorizes the action, not just the identity. Each call is checked against the agent's declared intent and policy, and sealed as an authorization verdict. RBAC asks "can this identity call this API?" — intent-based authorization asks "is this consistent with what the agent is supposed to be doing right now?"
A live, explainable 0–100 trust score for AI agents, recomputed every run from conformance, weighted violations against intent, configured controls, and evaluations. Cross a threshold and the agent is promoted; drift below one and it is auto-probated in real time.
Autonomy is a ladder, not a switch. Prove7 moves a process from deterministic to autonomous safely — calibrate against a baseline, run in scored standby, graduate on evidence, and regress automatically in under a minute when trust slips. Reversible by design.
Every action and verdict is appended to a hash-chained, per-tenant AI agent audit trail — detectable if broken, verifiable for an auditor, and mapped at runtime to the controls you answer for (SOX ITGC, ISO 27001 A.9, SOC 2 CC6).
Policy evaluates inline at agent entry, model inference, and connector-out — as an MCP gateway, an SDK decorator, or native gates — in observe (shadow) or hard-block mode, per control. One policy model, enforced wherever your agents run.
Third-party and bring-your-own agents are brought under the same seven gates: registered, given identity, bound to intent and RBAC, promoted through trust, enforced, and audited — so governance is uniform across native and external agents.
On top of the platform, Prove7 ships Agentic Application Packs: pre-built, governed agentic solutions that deliver a complete enterprise outcome end-to-end — the agents, the multi-agent workflow, the connectors, and the full trust plane — provisioned into your own tenant. The first pack, Access Review & Reconciliation, reconciles who should versus who actually has access, risk-ranks the findings, and seals every decision as evidence.
Agentic AI governance is the discipline of controlling and proving what autonomous AI agents do at runtime — giving each agent a verifiable identity, authorizing its actions against declared intent and policy, scoring its trustworthiness continuously, and sealing every action to a tamper-evident audit trail. Prove7 Control Vector™ delivers this as an Agentic Trust Infrastructure that governs agents inline, on every call.
Prove7 Control Vector™ is the Agentic Trust Infrastructure for agentic systems. It measures, enforces, and proves trust for AI agents in real time through seven governance gates — discovery, identity, intent, access, trust promotion, continuous enforcement, and end-to-end audit — so enterprises can run AI agents in production and prove every action to an auditor.
You govern AI agents in production by enforcing controls inline on every action rather than writing policy and hoping for it. Prove7 routes every agent call through a Governed Execution Layer that checks the agent's identity, authorizes its intent, enforces least-privilege access and policy, scores the run against a live trust score, and seals the result to a hash-chained audit trail — with a human approval gate on consequential actions.
Real-time AI governance evaluates and enforces policy at the moment an AI agent acts — at agent entry, model inference, and connector-out — in observe or block mode, instead of reviewing logs after the fact. Prove7 Control Vector™ performs real-time AI governance on every governed call and can revoke autonomy the instant an agent's trust score drops. Read more →
An AI agent trust score is a continuous, explainable 0–100 measure of how much an agent can be trusted, recomputed on every run from conformance, weighted policy violations against intent, configured controls, and evaluations. The Prove7 Agent Trust Score promotes an agent's autonomy as trust is earned and auto-probates it when trust slips. Read more →
See how Prove7 Control Vector™ governs an agent end-to-end: identity, intent, access, trust, enforcement, and sealed audit — in 30 minutes.
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