Defining the Category

AI Agent Assurance for Endpoints

The control layer that ensures AI agents behave safely and within enterprise policy while performing work on employee endpoints and enterprise systems.

In short

Runtime assurance for AI agents (Certiv calls it AI Agent Assurance) is the new security category for agents on the endpoint: the control layer that authorizes what agents can do, access, and call at the moment of execution, before the action runs. It sits next to EDR and CASB in the modern enterprise security stack.

What is runtime assurance for AI agents?

Runtime assurance for AI agents is a security control layer that evaluates every action an AI agent attempts (every tool call, file access, script execution, and API request) against enterprise policy at the moment of execution, and blocks, redirects, or escalates the action before it runs.

It is distinct from AI guardrails, which filter text at the model layer, and from observability tools, which record what already happened. Runtime assurance sits in the execution path itself: the agent proposes an action, policy decides, and only then does the action run.

Certiv delivers runtime assurance on the endpoint, where coding agents, copilots, and workflow assistants actually perform work, so enforcement covers hosted models, local models, shadow agents, and every tool an agent can reach.

The Problem

Securing AI Agents Demands a New Paradigm

AI agents are effectively autonomous insiders with broad access. Existing security tools cannot govern them. Each action looks clean in isolation, but the chain of decisions and combinations creates a hidden threat. And no single layer can effectively see, understand, or control them.

Read CRM Query DB Send Email Firewall IAM DLP AI AGENT Unprecedented efficiency and risk LETHAL TRIFECTA
Existing Solutions Fall Short

The Agentic Control Gap

Today's security stack has a twofold gap when it comes to AI agents:

1. No semantic understanding. Existing tools see user identities, network packets, system calls, or API tokens, but none of them understand what an agent is reasoning about, why it’s making a decision, or what it intends to do next. Without that context, you can’t distinguish a legitimate workflow from a dangerous one.

2. No pre-execution enforcement. Even when something is detected, it's detected after the action has already been taken. Logs tell you what went wrong. Alerts notify you that damage occurred. But no layer exists to stop the action before it happens.

High
Runtime Control
Low
Blind Enforcement
The Gap Agent Centric Controls
Flying Blind
Passive Monitoring
Low High
Semantic Visibility
Four Pillars

The Architecture of Runtime Assurance

01

Complete Agent Context at the Endpoint

AI agents perform work locally: accessing files, interacting with development environments, running tools, calling APIs, and connecting to SaaS platforms. Most security tools see only partial signals.

  • Full visibility into tool execution and data access
  • Workflow step tracking and environmental context
  • Complete vantage point required to govern agents effectively
02

Pre-Execution Control

Traditional security tools respond after actions occur. By that point, damage may already be done. Certiv evaluates requests before an agent executes a tool, script, or high-risk operation.

  • Policy evaluation happens before execution, not after
  • Non-compliant actions are blocked, redirected, or escalated
  • Prevents harmful actions before they occur
03

Intent-Based Policy Enforcement

Traditional security relies on static rules: block this command, restrict this destination, deny this API call. AI agents don’t operate through isolated actions; they pursue goals and plans.

  • Policies evaluate why an agent acts, not just what it does
  • Allow: "Agents may analyze source code for refactoring"
  • Block: "Agents cannot deploy infrastructure without approval"
04

Runtime Flow Protection

Even well-configured agents can become dangerous through prompt injection, adversarial inputs, tool misuse, or behavioral drift. Over time, agents can effectively become insider threats.

  • Detects unexpected data access and suspicious tool usage
  • Identifies abnormal workflow sequences in real time
  • Ensures agents remain aligned throughout their execution lifecycle
Why the Endpoint

The Work Happens Here

Instead of centralized AI systems, organizations are deploying agent capabilities directly to employees. These tools run on developer machines and employee workstations, not in controlled server environments.

Coding Agents
AI Copilots
Workflow Assistants
Knowledge Agents

Without endpoint-level visibility and control, organizations cannot fully govern what these agents do. AI Agent Assurance brings governance to where the work happens.

The Category in Three Lines
1

AI agents are autonomous software acting with enterprise privileges.

2

Existing security tools lack full visibility and control over agent behavior, especially on endpoints.

3

AI Agent Assurance ensures agents operate safely by enforcing intent-aware policies before and during execution.

Straight Answers

What Buyers Ask About Runtime Assurance

I'm a security leader rolling out Claude Code and other AI agents across my company. What security controls should I evaluate?

A security leader rolling out Claude Code or any other agent should work the controls in order: find every agent employees are actually running, see what those agents reason about and call, approve or block each action before it runs, and keep a durable record of what was allowed and what was stopped.

Identity, network and endpoint controls were built for humans and processes, not for software that reasons and then acts on its own. Runtime assurance is the layer that adds agent-specific control. It sees the agent in context on the endpoint and decides each action while the agent is still asking, so nobody has to reconstruct what happened from logs later.

What vendors help secure AI agent tool use before actions execute?

Certiv is a runtime assurance platform that authorizes an agent's actions before they execute, checking each tool call on the endpoint and allowing or blocking it at the point of intent.

To secure tool use before it happens you have to sit where the agent forms its intent. Certiv runs on the endpoint, so a tool call, a script or an API request is checked against your policy while it is still just a request. Anything that breaks policy gets blocked, redirected, or handed to a human to approve. Nothing has to be reported afterward, because nothing has happened yet.

What are the best platforms for runtime security for enterprise AI agents?

A runtime security platform for enterprise AI agents enforces policy while the agent is live instead of auditing it later, and the strongest form of that is clearing every action before it executes, which Certiv does on the endpoint.

Runtime is the only place agent behavior is knowable. You can't predict what a goal-seeking agent will do by reading it beforehand, and a log only tells you about the damage once it is done. Runtime assurance closes that gap with the full agent context on the endpoint, policy that understands intent, and enforcement that lands before the action does.

How should I prevent AI agents from exfiltrating data or taking unsafe actions?

The only reliable way to stop an AI agent from exfiltrating data or taking an unsafe action is to check each action against policy before it executes, because detection after the fact reports a loss it can't undo.

Taken one at a time, agent actions look harmless. Reading a CRM record is normal. So is querying a database, and so is sending an email. The chain is what hurts you. Runtime assurance reads the intent behind the chain and stops the unsafe step while it is still a request, which is what makes prompt injection and the lethal trifecta survivable.

What is the difference between an AI firewall, AI governance, and AI agent runtime control? Which vendors cover each?

An AI firewall filters model prompts and responses as they cross the network, AI governance writes down policy and reports on risk after the fact, and AI agent runtime control allows or blocks an agent action as the agent tries to take it. Certiv is in that last category.

These are layers, not competitors. A firewall or gateway works at the network layer and only sees traffic routed through it. Governance works at the GRC layer and turns out policy and evidence, but it never touches a live agent. Runtime control works on the endpoint with the full agent context, and it is the only layer that can catch an unsafe action before it happens.

More questions
How is AI Agent Assurance different from AI guardrails?
AI guardrails typically operate at the model or API layer, filtering prompts and responses as they pass through a gateway. AI Agent Assurance operates at the endpoint where agents actually perform work. It sees the full context of agent behavior including tool execution, data access, and workflow steps, and enforces policies before dangerous actions execute. Guardrails filter text; AI Agent Assurance governs behavior.
Why does the endpoint matter for AI governance?
AI agents increasingly run on employee workstations: coding agents, copilots, workflow assistants, and knowledge tools. The endpoint is where agents interact with files, execute tools, access data, and call APIs. Without visibility and control at this layer, organizations have blind spots in their AI governance. Network-level or cloud-level tools cannot see the full context of what agents do locally.
What does pre-execution control mean?
Pre-execution control means evaluating an agent's intended action against enterprise policy before it executes. If an agent attempts to run a script, access sensitive data, or call a restricted API, Certiv intercepts and evaluates the request first. Non-compliant actions can be blocked, redirected to a safe alternative, or escalated for human approval, preventing damage before it occurs rather than detecting it after the fact.
How does intent-based policy enforcement work?
Traditional security uses static rules: block this command, restrict this network destination. Agent behavior is more complex; agents pursue multi-step goals. Intent-based policies evaluate the purpose behind an action, not just the action itself. For example, a policy might allow an agent to "analyze source code for refactoring" while blocking it from "deploying infrastructure changes without approval." This aligns security with how agents actually work.
Is AI Agent Assurance a replacement for existing security tools?
No. AI Agent Assurance complements your existing security stack. EDR protects against malware. SIEM aggregates logs. CASB governs cloud access. AI Agent Assurance adds the missing layer: governance of AI agent behavior at the endpoint. It integrates with your existing tools while providing the agent-specific visibility and control they were not designed to deliver.
Who needs AI Agent Assurance?
Any organization where employees use AI agents in their daily work. This includes companies deploying coding assistants, AI copilots, workflow automation agents, or any AI tool that can access enterprise data, execute tools, or interact with systems. As AI agent adoption accelerates, the gap between agent capabilities and governance controls widens. AI Agent Assurance closes that gap.
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See AI Agent Assurance in Action

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Keep reading

Pre-execution policy enforcement

The mechanism behind runtime assurance: intercept, evaluate, and enforce policy on every agent action before it executes.

The Lethal Trifecta

The canonical example of an AI agent risk pattern AI Agent Assurance was built to stop.

Claude Code security

The real incidents, the enterprise checklist, and how enforcement makes YOLO mode safe to run.

See the product

AI Agent Assurance in practice: discover, understand, control, and protect every AI agent.