Clear, source-of-truth definitions for the terms behind governing AI agents in production — from Agentic Trust Infrastructure to runtime agent control and the Seven Gates.
Accountable Autonomy™ is Prove7's positioning for governed agentic AI: an AI agent earns the right to act by being able to answer for every action — as it acts, not after. Every action passes governance inline, ships with its own proof, and earns the autonomy it gets, which makes the autonomy reversible and defensible. It is the promise behind the Prove7 sign-off, Answers as it acts — and the standard the Prove7 Control Vector™ platform is built to meet.
Agentic Trust Infrastructure is the runtime layer that measures, enforces, and proves trust for AI agents. It issues each agent a verifiable identity, authorizes its actions against declared intent and policy, scores its trustworthiness continuously, enforces policy inline on every call, and seals every action to a tamper-evident record. Prove7 Control Vector™ is an Agentic Trust Infrastructure — the thing that lets an enterprise run AI agents in production and prove what they did.
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 combines identity, intent-based authorization, least-privilege access, continuous trust scoring, real-time policy enforcement, and tamper-evident audit into one governed path of execution.
Real-time AI governance is enforcing policy at the 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. It is the difference between observability (knowing what an agent already did) and control (stopping it before it does the wrong thing).
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 inline enforcement on every 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.
An agent trust score is a continuous, explainable 0–100 measure of how much an AI agent can be trusted, recomputed every run from conformance, weighted policy violations against intent, configured controls, and evaluations. Trust scoring lets autonomy expand as trust is earned and contract automatically when it slips — the basis for every promotion and every regression.
Intent-based authorization authorizes an AI agent's action against its declared intent and policy — asking whether the action is consistent with what the agent is supposed to be doing right now — rather than only checking whether its identity may call an API. It closes the gap that role-based access control (RBAC) leaves open for autonomous agents, and every verdict is recorded as evidence.
Non-human identity governance is the governance of machine and AI-agent identities — issuing each a cryptographic identity, enforcing least-privilege access, and auditing their actions. It matters because machine identities now vastly outnumber human ones and most carry excessive, unreviewed privilege. Prove7 gives every agent a SPIFFE identity from a platform certificate authority.
The Governed Execution Layer is the Prove7 runtime that wraps every AI agent call and enforces the full governance chain — identity, intent, access, policy, trust, and evidence — before the action is allowed to happen. It runs as an MCP gateway, an SDK decorator, or native gates, so one policy model is enforced wherever your agents run.
The Seven Gates are Prove7's seven sequential governance gates every agentic application must pass: (1) Discovered, (2) Identity assigned, (3) Intent attributed, (4) Access resolved, (5) Trust promoted, (6) Continuously enforced, and (7) End-to-end audited. Agents built on Prove7 inherit all seven by construction; external agents must pass through every gate to reach the governed core.
The Trust Transfer Framework is Prove7's method for moving a process from deterministic to autonomous safely: calibrate against a known-good baseline, run the agent in scored standby, graduate it on sustained evidence, and regress it automatically in under a minute when trust slips. Autonomy is the amount an agent has measurably earned — and always reversible.
An Agentic Application Pack is a pre-built, governed agentic solution that delivers a complete enterprise outcome end-to-end — the agents, the multi-agent workflow, the connectors, and the full trust plane — provisioned into a customer's own tenant like a marketplace app. The first pack is Access Review & Reconciliation.
An AI agent audit trail is a hash-chained, tamper-evident, per-tenant record of every AI agent action and governance verdict — detectable if broken, verifiable for an auditor, and mapped at runtime to compliance controls such as SOX ITGC, ISO 27001 A.9, and SOC 2 CC6. In Prove7, compliance evidence is a by-product of governance, not a separate project.
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