Defining the Category

Runtime Assurance for AI Agents

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

Visibility, intent-aware policy enforcement, and pre-execution safeguards that prevent agents from executing dangerous actions before damage occurs.

The Shift

AI Agents Are No Longer Experiments

AI agents have moved from research demos to operational software used by employees every day. They now perform real work with real consequences, and they increasingly run on employee endpoints, not just in controlled cloud environments.

Write & modify code
Access enterprise data
Execute tools & scripts
Automate workflows
Interact with APIs & systems

AI agents are effectively autonomous insiders with broad access.

Existing security tools cannot govern them.

The Problem

The AI Security Market Is Fragmented

What Vendors Claim

AI governance
AI runtime security
Agent identity
Prompt guardrails

Where They Actually Operate

Model APIs
Filtering requests at the API gateway
Cloud Environments
Monitoring infrastructure logs
Application Gateways
Proxy-based request inspection

These approaches provide incomplete visibility into real agent behavior. They cannot see or control agent activity where work actually happens: the endpoint.

The Missing Layer

Endpoint Runtime Assurance

AI Development Controls
Prompt filters · Model guardrails
Infrastructure Security
Network · Cloud · Identity
Runtime Assurance for AI Agents
Behavior · Intent · Execution Control
Certiv
Enterprise Systems
Data · APIs · Workflows

Certiv brings governance directly to where agents operate: the workstation. Full visibility into agent behavior. Controls that are impossible from remote vantage points.

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. Runtime Assurance brings governance to where the work happens.

The Analogy

A Familiar Pattern

Established

EDR is to malware

↓
New

Runtime Assurance is to AI agents

Established

Runtime security is to cloud workloads

↓
New

Runtime Assurance is to AI agents

It governs behavior while software is executing, ensuring safety during operation.

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

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

FAQ

Frequently Asked Questions

Expand to view common questions.

How is Runtime 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. Runtime 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; Runtime 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 Runtime Assurance a replacement for existing security tools?
No. Runtime Assurance complements your existing security stack. EDR protects against malware. SIEM aggregates logs. CASB governs cloud access. Runtime 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 Runtime Assurance for AI Agents?
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. Runtime Assurance closes that gap.
Learn More

See Runtime Assurance in Action

Discover how Certiv brings governance to where AI agents actually work.

Book a Demo See the Product