Runtime
governance for
autonomous AI agents.
A policy enforcement layer that sits between your agent framework and the actions it takes, before they execute. Deterministic. Sub-millisecond. Built for production.
The Problem
Safety today is probabilistic. It should not be.
85%
use AI agents in production
Agents are deployed
The majority of organizations have already deployed AI agents into production. The question is not whether agents are in your systems, but whether you can see them.
68%
cannot distinguish agent from human
You cannot see what is acting
Most organizations cannot confidently tell whether an action was taken by a human or an AI agent. If you cannot see the actor, you cannot govern the action.
74%
agents get more access than needed
Agents are overprivileged
The vast majority of agents operate with more permissions than their tasks require. The principle of least privilege is not being applied to machine identities.
22%
apply identity governance
Governance is not keeping pace
Only a small fraction of organizations apply proper access and identity governance to AI agents. The gap between deployment and security is widening.
McKinsey State of AI 2025 / Cloud Security Alliance & Aembit 2026
The Platform
Six modules. One governance layer.
Policy Engine
Evaluate every tool call, resource access, and inter-agent message against policy before execution. Sub-millisecond. Deterministic.
Zero-Trust Identity
Enforce cryptographic credentials, continuous trust scoring, and strict delegation limits on every actor. Spoofing and escalation have real defenses.
Execution Supervision
Contain failures with privilege rings, resource limits, and kill switches. Recover cleanly through saga orchestration with automatic compensation.
Agent Reliability
Apply SLOs, error budgets, circuit breakers, and chaos engineering to agent fleets. Debug with replay. Recover with precision.
MCP Security Scanner
Scan MCP definitions for poisoning, typosquatting, and embedded directives. Verify third-party tools before they touch your agents.
Governance Visibility
Surface shadow agents, manage full lifecycles from provision through decommission, and maintain operational awareness through real-time dashboards.
How It Works
Gate every action.
Governance overhead: <0.1 ms per action, roughly 10,000x faster than an LLM API call.
01
Agent Initiates
An agent attempts a tool call, resource access, or inter-agent communication.
02
Policy Evaluates
The governance layer checks the action against your policies before execution. Latency is sub-millisecond.
03
Action Authorized
Permitted actions proceed. Blocked actions are stopped deterministically with full reasoning provided.
04
Decision Recorded
Every outcome is logged with complete context, agent identity, and policy rationale. Full audit trail by default.
Works With Your Stack
Early Access
Ship agents with
confidence.
We are running structured interviews with platform engineers, security teams, and SREs. If you are shipping or planning to ship agentic applications, we want to talk.