Agent Enforcer shield
Enterprise AI Policy Enforcement

Stop the Slop.

Enforce what your AI agents are allowed to do before they do it. Agent Enforcer locks your security policies, compliance controls, and coding standards into every AI session, automatically, at the system level.

“When your organization deploys AI coding assistants, who's making sure they follow your rules?”

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The Problem

Ungoverned AI is already writing your code

Organizations are deploying AI coding assistants at scale. These models have no inherent knowledge of your security controls, compliance requirements, or internal standards.

No guardrails at session start

Every AI session begins with a blank slate. No policies. No security context. No standards.

Inconsistent, non-compliant output

Developers prompt AI differently, producing wildly divergent and non-compliant code.

Vulnerabilities ship silently

Security flaws get written and shipped before any human ever reviews them.

Thin audit trails

Accountability is unclear. Auditors ask how you govern AI, and there is no answer.

Token costs balloon

Unguided agents loop through trial and error, burning budget on wasted iterations.

Without enforcement, every risk above compounds across every developer, every day.

The Solution

System-level enforcement. Zero developer friction.

Agent Enforcer installs as a lightweight system service on developer workstations and CI/CD environments. It pulls your approved policy and applies it to every AI session, automatically.

Policy Source

Enforcement Management UI

NIST controls, coding standards, and security policies are uploaded as plain-English documents.

Distribution Engine

Configuration Engine

Converts policy docs into structured guidance for your coding agents: instructions, settings, skills, and commands.

Enforcement Layer

Every Developer. Every Session.

Agent Enforcer runs on every workstation, active before a single line of code is written.

Automatic · Continuous · Sync

Defined once
Enforced everywhere

Define once. Enforce everywhere, automatically.

  • Security requirements baked in before code is written
  • Compliance controls active on every AI session
  • Coding standards applied uniformly across all developers
  • No developer action required. System-level enforcement
  • Policy updates propagate to every endpoint automatically
  • Works with existing coding agent deployments. No rearchitecting
Three Pillars of Value

Security, savings, and standards, enforced by default

Agent Enforcer delivers compounding value from day one: hardened security posture, dramatic token savings, and uniform coding standards across your entire organization.

01Security & Compliance

Controls active before code is written

AI agents do exactly what they're told. If they aren't told your security requirements, they'll write code that ignores them. Agent Enforcer embeds your controls directly into the AI's operating context.

NIST 800-53CMMCFedRAMPSTIG
  • Parameterized queries only. SQL injection blocked at the source
  • Secrets via environment variables. No hardcoded credentials, ever
  • Non-root container execution. Least-privilege Docker by default
  • HTTPS-only endpoints. Enforced protocol standards
  • Approved library lists. Unapproved dependencies flagged
  • Custom policy documents for your own internal standards

Monthly token spend

Conservative
Unenforced AI
−28%
With Enforcer
02Token Cost Reduction

Up to 0% savings on developer AI spend

Unguided agents iterate wastefully. They assume, get corrected, retry, and loop. Every exchange burns tokens. Agent Enforcer eliminates the most expensive loops by giving the AI the right context from the first prompt.

$0.0M
Conservative annual savings*
$0.0M
Likely annual savings*

*Modeled on $1M/month organization-wide token spend ($12M/year): 28% conservative, 35% likely reduction.

03Uniform Coding Standards

Same task. Same AI. Different rules.

When 50 developers prompt the same AI differently, you get 50 different patterns and 50 different security postures. Agent Enforcer standardizes output across your entire development organization.

Demonstrated result: enforced AI sessions produced zero critical vulnerabilities versus two in unenforced sessions. Same model, same task.

Without Enforcer
def get_user(id):
  q = f"SELECT * FROM
    users WHERE id={id}"
  return db.run(q)
  # no types
  # no docstring
With Enforcer
def get_user(id: int) -> User:
  """Fetch a user by id."""
  return db.query(
    "SELECT * FROM users "
    "WHERE id = %s", (id,),
  )
How It Works

From policy to enforcement in four steps

Defined once by your team, then enforced everywhere, continuously and automatically.

  1. 1

    Define Policy

    Your team authors enforcement documents in plain English. Security requirements, compliance controls, and coding standards.

  2. 2

    Generate Configuration

    A configuration engine converts policy docs into structured guidance for your coding agents: instructions, settings, skills, and commands.

  3. 3

    Distribute Automatically

    Agents on every developer machine sync enforcement config automatically. No developer action required.

  4. 4

    Enforce at Session Start

    Every AI coding session starts with your policies already active. Compliant by default.

Deployment Profile

Platform
Rocky Linux / RHEL 9 (RPM)
Agent size
< 1 MB installed
Network
Outbound HTTPS only
Air-gap compatible
Yes, configurable endpoint
Sync interval
Configurable per fleet
User action required
None after install
Credentials
IAM role, no user secrets
Audit trail
Sync logs + version history

Supported Operating Systems

Linux Available
macOSComing soon
WindowsComing soon

Prefer to hand it off? White-glove policy service

No bandwidth to author and maintain policy in-house? Our enforcement specialists will define, upload, and continuously maintain your enforcement posture for you, so your standards stay current as your stack and compliance requirements evolve.

Talk to a Specialist
Disconnected Environments

Agents fly blind in the air gap

On the open internet, an AI agent can lean on assumptions. Inside an air-gapped enclave, those assumptions quietly break, and the agent doesn't know it.

Unknown network topography

The agent has no map. It probes blindly for services and hosts that may not live where it expects them to.

Unresolved

URLs that don’t route

Endpoints it assumes are reachable time out, resolve to nothing, or sit behind a proxy it never anticipated.

Unresolved

Cloud features that aren’t there

Managed services and cloud APIs the model instinctively reaches for simply do not exist inside the enclave.

Unresolved

Libraries and repos behind upstream

Internal mirrors and package registries lag public sources, so its first-choice versions and imports fail.

Unresolved
The Compounding Cost

Every wrong assumption becomes token swirl

The agent retries dead routes, hunts for services that were never deployed, and cycles through versions that don't exist. Each failed guess spawns another, so churn in a disconnected environment runs even hotter than on the open internet, burning budget on loops that can never resolve.

Agent Enforcer front-loads ground truth. Your real network map, approved endpoints, available services, and internal package sources are pinned into every session, so the agent stops guessing and starts building against reality.

Founding Partner Proposal

The economic case is decisive

A full Organization-tier deployment for up to 1,000 enforcement agents, dedicated Managed Enforcement Specialists, and a Founding Partner rate. Projected to return its cost in token savings alone within 12 to 18 months.

Year 1 Investment
$0.0M

Founding Partner rate. 23% below standard.

Year 2+ Annual
$0.00M

Standard renewal rate, per year.

Cost savings breakdown

Modeled against $12M annual token spend.

Break-even $2.5M
$0$5M$10M$15M
  • Token Savings$3.36M
  • Security Incident Savings$5M+
  • Recovered Dev Hours$5M+

Token savings assume a 28% efficiency gain on $12M annual spend, exceeding Year 1 investment on efficiency alone.

Security risk reduction

In controlled testing, unenforced sessions averaged one critical vulnerability per task. Enforced sessions produced zero.

LOWHIGH
Without Enforcer
LOWHIGH
With Enforcer
  • Hardcoded credentials
  • SQL injection
  • Privilege escalation

Agent Enforcer does not replace your security team. It makes your security team's requirements impossible for AI agents to ignore.

Plans

Plans built for every scale

Choose the plan that matches your workforce. Every plan includes system-level enforcement, automatic distribution, and full audit trails.

Division

Up to 250 seats

Contact for pricing
  • Centralized policy configuration
  • Cloud-native deployment
  • Automatic rule propagation
  • Fine-grained fleet management
Contact Sales
Most Common

Organization

Up to 1,000 seats

Contact for pricing
  • Everything in Division
  • Air-gap deployment support
  • Agents for custom operating systems
  • Hands-on initial policy uploading
Contact Sales

Enterprise

Up to 5,000 seats

Contact for pricing
  • Everything in Organization
  • Prioritized feature requests
  • Dedicated training
  • 1 managed enforcement specialist
Contact Sales

Unlimited

Unlimited seats, organization-wide, with a dedicated account team.

Dedicated account team

Founding Partner Rate available for the first organization engagement. Contact for details.

Agent Enforcer

Govern your AI before it's a liability.

Auditors are already asking how you govern your AI. Agent Enforcer is your answer. Request a briefing with an enforcement specialist.

Works with your existing tooling
Claude CodeKiroCursorGitHub Copilot