The AI That
Governs AI.
The infrastructure your board must have for governing AI agents.
The infrastructure your board must have for governing AI agents.
This panel used to show a 99.9 Agent Aegis score for Agent Shield beside estimated scores for other platforms. The Agent Shield score came from runs that counted escalations and error responses as blocks and recorded no false-positive rate, so it did not measure detection, and the other platforms’ scores were estimates from public documentation, never measured. Both are withdrawn. A detection figure will be published only from a run through the live endpoint that scores blocked, held and approved separately and reports its false-positive rate. Benchmark status
AgentGoverning, which publishes the AGS standard and the Agent Aegis audit, is operated by Imperium Business Intelligence, which also develops Agent Shield.
The shift from AI that responds to AI that acts is not an incremental improvement. It is the most significant transition in artificial intelligence since the arrival of large language models — and it is happening now, in production, without the governance infrastructure to match.
Agents are initiating actions, making decisions, and operating autonomously across critical systems. No constitutional framework existed to govern them. Until Agent Shield.
Agent Shield is being rolled out across multiple products. The organisations that move early will define what responsible agentic AI looks like. The ones that wait will be retrofitting compliance onto systems already in production.
That window is closing.
Agent Shield enforces hard, deterministic boundaries on autonomous agent actions — designed for prevention by construction, not just probabilistic detection. Built against the AGS v3.1 governance standard.
AI agents are not software to be secured. They are operational entities — they initiate transactions, make decisions, and create operational exposure. They require the same governance infrastructure as any operational entity within defined authority limits: governance controls, operational controls, and authenticated audit trails.
The governance infrastructure for autonomous AI agents is still being defined. Agent Shield is built to be the constitutional framework for artificial intelligence in the economy.
This block used to report 600 attacks from GPT-4o, Gemini 2.5 Flash and Grok-3 with 0 bypasses. Those runs counted an escalation or an error response as a block and recorded no false-positive rate, so they did not measure detection, and the figures are withdrawn (November 2026).
A new figure will come from a run through the live endpoint that counts blocked, held and approved separately, excludes errors, includes a benign set with its false-positive rate, and records its corpus and code version. Benchmark status
Every agent action passes through multiple independent evaluation layers, and any one of them can stop it: an action is authorised only if no layer blocks or holds it. An agent cannot skip a layer for an action routed through Agent Shield.
Validates every action against the agent's authorised operational scope. Evaluates impact in real time using proprietary methodology.
Non-negotiable constraints. Cannot be overridden by any agent instruction, any business justification, or any other layer. A violation is blocked; the kill switch suspends an agent at once.
Looks for multi-step agent patterns that pass individual rule checks while pursuing misaligned goals, so they can be stopped before they complete.
Evaluates why the agent acts, not just what it does. Advisory in isolation, critical within the combined pattern analysis.
Three roles. Three sets of questions. One governance framework.
Agent Shield is not a product you trial. It is infrastructure you govern with. Request a governance assessment with your team.
Agents are not software to be secured. They require governance infrastructure.