AI Agents with Frontegg Entitlements

AI agents often act autonomously on behalf of users, executing actions like updating tickets, posting messages, or pulling reports. To ensure every action is authorized according to your business logic, Frontegg provides a native integration between its Entitlements Engine and AI Agents using MCP tools.

This guide covers:

1. Deploying the Entitlements Agent (with MCP support)
2. Connecting your AI agent to MCP tools
3. Enforcing policy checks autonomously through prompt engineering

Step 1: Deploy the Entitlements Agent with MCP

Start deploying the Entitlements Agent in your environment using Helm with custom values.yaml configuration file:

1. Create values.yaml

In order to obtain fronteggClientCredentialsOauthClientId and fronteggClientCredentialsOauthSecret, please follow these instructions.

env:
  # PDP Agent container environment variables
  pdpAgent:
    fronteggRegion: "eu" / "us" / "ca" / "au"
    fronteggClientId: "FRONTEGG_CLIENT_ID"
    fronteggClientCredentialsOauthClientId: "FRONTEGG_CLIENT_CREDENTIALS_OAUTH_CLIENT_ID"
    fronteggClientCredentialsOauthSecret: "FRONTEGG_CLIENT_CREDENTIALS_OAUTH_SECRET"
    pollingMinDelay: "30"
    pollingMaxDelay: "60"

  # MCP Server container environment variables
  mcpServer:
    pdpUrl: "http://localhost:8181"

2. Install using Helm

helm repo add frontegg https://frontegg.github.io/helm-charts
helm repo update
helm install entitlements-agent frontegg/entitlements-agent --values values.yaml

This deploys:

• The Entitlements Agent (PDP) – fetches and syncs policies from Frontegg
• An MCP Server – exposes authorization tools over HTTP for local access by your agent

Step 2: Connect your AI Agent to MCP

You can now connect your AI Agent to the MCP server to access the entitlement tools programmatically.

Example (TypeScript):

import { Client, StreamableHTTPClientTransport, loadMcpTools } from 'mcp-sdk';

const entitlementsMcpClient = new Client({
  name: 'ent',
  version: '1.0.0',
  serverUrl: 'http://127.0.0.1:8182/mcp', //this is the MCP server URL deployed in the helm chart before
});

await entitlementsMcpClient.connect(
  new StreamableHTTPClientTransport(new URL('http://127.0.0.1:8182/mcp'))
);

const entitlementsTools = await loadMcpTools('entitlements', entitlementsMcpClient);

Available MCP tools

After connecting, your agent can use the following tools:

Step 3: Enforce Authorization Using Prompt Instructions

To ensure your agent performs checks before any operation, provide it with the following system prompt:

Important! oauth tools do not require an action, you can execute them directly.

Important! before executing any tool other than the oauth* tools, verify the tool is allowed by executing the 'is-entitled-to-entity-action-tool' using the user ID from the context and the entities from the 'entitlements-entities-tool'.

Important! before executing the 'is-entitled-to-entity-action-tool', you must call the 'entitlements-entities-tool' MCP tool.

The default entityType of the executing entity when using entitlements is 'user'.

This makes the AI agent:

• Call entitlements-entities-tool first to discover the available schema
• Use the is-entitled-to-entity-action-tool before executing any action
• Enforce policy autonomously, without relying on backend API enforcement

Summary

By combining Frontegg Entitlements with MCP tooling, AI agents become secure, policy-aware entities that enforce authorization with no developer intervention required per action.