The Certiv Approach

Securing Agent Intent Demands a New Paradigm

AI agents act with broad access, and existing tools only see the traffic — never the intent. Certiv reads what an agent intends and acts before it executes.

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Running on your macOS endpoint
  • Reads intent
  • Understands why
  • Acts before execution
In short

Existing security tools see network traffic, not agent intent. Certiv runs on the endpoint where agents act, so it can read what an agent is about to do, understand why, and block it before it happens — visibility and enforcement in one place.

Existing Solutions Fall Short

The Agentic Control Gap

Two gaps define today's security stack:

1. No semantic understanding. Existing tools see identities, packets, and API tokens. None understand agent intent. Without it, you can't tell a legitimate workflow from a dangerous one.

2. No pre-execution enforcement. Detection happens after the fact. Logs record what went wrong. Alerts confirm damage. Nothing stops it before execution.

High
Runtime Control
Low
Blind Enforcement
The Gap Agent Centric Controls
Flying Blind
Passive Monitoring
Low High
Semantic Visibility
Why the Endpoint

The Work Happens Here

Organizations don't deploy AI centrally. Agents run on developer machines and employee workstations.

Coding Agents
AI Copilots
Workflow Assistants
Knowledge Agents

Without endpoint visibility, you can't govern agents.
AI Agent Assurance governs where the work happens.

Control at the Point of Intent

Certiv sits where agents reason and act

Certiv is the industry's first AI Agent Assurance Layer for the endpoint — an intent-based policy engine that stops risky actions before they execute.

Complete Visibility + Execution Control + Intent-Based Policy + Decision Engine
Complete Visibility

See the Full Action Context

Agent visibility requires all four dimensions. Certiv tracks the complete action context for every agent and session.

Agent Session 1 2 3 4
1

Host / Process / User Prompts

Runtime environment, identity, and user prompts that launch and direct agent behavior.

2

Model Chain of Thought

Reasoning steps and logic the model follows before acting.

3

Tool Calls & Data

APIs, MCP servers, file systems, and data sources agents invoke.

4

Agent-to-Agent Calls

Sub-agent sessions, delegated tasks, and their cascading tool calls.

Execution Control

Govern Actions Before They Run

Certiv intercepts every tool call, API request, and system interaction, evaluating each against intent-based policy and blocking risky actions before they run.

CERTIV Workstation AI Agent | Browser GOVERNED EXECUTION REMOTE / CLOUD SaaS Apps Cloud DB CI / CD Prod APIs 3rd Party Agent Skills PRIVATE NETWORK GraphQL Webhooks MCP Remote OAuth SDK Calls VPN / Tunnel ON MACHINE File System Shell / CLI Local DB IDE Plugin Git MCP Local
Intent-Based Policy

Policy Built for How AI Actually Behaves

Fixed rules can't handle non-deterministic behavior. Certiv's intent-based policy evaluates purpose, not just pattern, keeping policies meaningful as behavior evolves.

Understand Intent Parse purpose, not just patterns Detect Drift Flag when behavior deviates from intent Enforce Block, pause, or approve in real time
Decision Engine

Smart, Cloud-Scale Decisions

Actions flow through the Certiv Brain, a cloud-scale engine scoring risk, evaluating policy, and detecting threats in real time.

CERTIV BRAIN Host / User Prompts Local or Remote Models Tool Calls & Data AI Agents, Browsers & Flows
FAQ

Frequently Asked Questions

Expand to view common questions.

Why can't traditional security tools like EDR, CASB, or network proxies secure AI agents?
Traditional tools were built for humans, not agents. Network proxies see packets, not reasoning. EDR monitors system calls, not intent. CASB covers browser SaaS; agents bypass it via APIs. None can see what an agent is thinking or about to do.
What is runtime agent security and why does it matter?
Runtime agent security means enforcing policy where agents reason and act: the application layer on the endpoint. It's the only position to see reasoning chains, prompt content, tool calls, and data flows before execution. Certiv operates there, intervening before risky actions happen rather than investigating after.
How is Certiv different from AI observability platforms and LLM guardrails?
Observability tools log what happened: useful for debugging, useless for enforcement. LLM guardrails filter model I/O but miss tool calls, multi-step chains, and execution. Certiv acts at the point of action with full context, enforcing policy across the workflow, not just model conversations.