(Reference Architecture & Personal Technical Exploration)

This repository documents a personal, non-commercial technical exploration of governance patterns for AI systems using the Model Context Protocol (MCP) when interacting with enterprise platforms such as ServiceNow.

The focus of this work is architectural: how to introduce policy enforcement, auditability, and execution control between AI intent and downstream system actions.

Important This repository intentionally contains documentation and reference materials only. It does not provide a public implementation, product, or deployable solution.

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What This Is

• A reference architecture for MCP-based AI integrations
• A learning exercise focused on AI governance and control-plane design
• A set of patterns and diagrams illustrating how AI intent can be safely translated into system execution

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What This Is Not

• A product, platform, or SaaS offering
• An official ServiceNow integration
• A replacement for the ServiceNow SDK, APIs, or tooling
• A production-ready or supported deployment

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Architectural Overview

At a high level, the exploration applies a familiar Control Plane / Data Plane mental model:

• Control Plane Defines what actions are allowed, under which conditions, and with what constraints.

• Data Plane Handles the execution of approved actions and emits audit-ready signals.

This separation helps make AI-initiated actions:

• explicit
• reviewable
• bounded

without embedding governance logic directly into every integration.

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Conceptual Request Flow

1. An AI system expresses intent using MCP
2. The request is evaluated against declared capabilities and policies
3. Approved actions are executed via standard ServiceNow APIs or SDK-based endpoints
4. Execution context and outcomes are recorded for traceability

All system names and flows are shown as examples only.

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Visual References

This repository includes architecture diagrams and walkthroughs that illustrate these patterns:

• Conceptual Request Lifecycle — from AI intent to ServiceNow execution
• Control Plane vs Data Plane Separation — governance vs execution
• Point-to-Point vs Gateway Pattern — centralized policy enforcement

All visuals are labeled as reference architecture and are provided for learning purposes.

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Status

This work is ongoing and intentionally scoped as a personal technical exploration. There is no public roadmap, release plan, or commercialization intent.

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🔄 MCP Gateway Flow Cycle

This document outlines the complete request-response lifecycle within the MCP Gateway architecture, detailing how user requests are processed, secured, and executed against ServiceNow.

⚡ The 8-Step Flow

The lifecycle of a single request travels through four distinct zones: User, Client, Gateway, and ServiceNow.

1. User Makes Request

Actor: User

• The user interacts with their AI assistant (Claude, Gemini, etc.) using natural language.
• Example: "Create a P1 incident for the database outage."

2. Request to MCP Client

Actor: AI Agent (MCP Client)

• The AI agent receives the natural language prompt.
• It identifies that external tools are needed to fulfill the request.
• It prepares to communicate with the configured MCP Gateway.

3. Client Requests Tools

Actor: AI Agent → Gateway

• The client connects to the Gateway (via stdio or HTTP) and requests the list of available capabilities (tools).
• Protocol: tools/list

4. Gateway Returns Tools

Actor: Gateway → AI Agent

• The Policy Engine intercepts the request.
• It evaluates the user's identity and permissions.
• It filters the global capability catalog, returning only the tools this specific user is allowed to see.

5. Tool Invocation

Actor: AI Agent → Gateway

• The AI Agent selects the appropriate tool (e.g., create_incident) and extracts parameters from the user's prompt.
• It sends a JSON-RPC request to the Gateway.
• Protocol: tools/call

6. Gateway → ServiceNow

Actor: Gateway → ServiceNow API

• Pre-Execution Check: The Gateway validates the action against the active policy (checking for read-only modes, required approvals, etc.).
• The ServiceNow Connector translates the request into a specific ServiceNow API call (REST or SDK).
• It executes the authenticated request against the ServiceNow instance.

7. ServiceNow Response

Actor: ServiceNow → Gateway

• ServiceNow returns the raw result (e.g., the created incident record).
• Post-Execution: The Gateway receives the data and applies Field Filtering policies to redact sensitive information before passing it back.

8. Result to User

Actor: AI Agent → User

• The Gateway returns the sanitized JSON result to the AI Agent.
• The AI Agent interprets the structured data and generates a natural language response for the user.
• Result: "I've successfully created Incident INC12345. You can view it here..."

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🔐 Security Layers Throughout

The Gateway architecture enforces security at every stage of the lifecycle, ensuring a "Defense in Depth" strategy.

Layer	Location	Description
Authentication	Client ↔ Gateway	Verifies the identity of the calling AI Agent (OAuth/API Key).
Authentication	Gateway ↔ ServiceNow	Manages secure, scoped credentials for backend access.
Policy Enforcement	Capability Discovery	Hides unauthorized tools from the user entirely.
Policy Enforcement	Execution Request	Validates specific actions (e.g., "Can User A create P1 incidents?").
Audit Logging	Decision Points	Records every ALLOW/DENY decision, input payload, and outcome for compliance.
Safety Mechanisms	Global Guardrails	Includes Kill Switch, Read-Only Mode, Rate Limiting, and Approval Workflows.

Disclaimer

This repository reflects a personal learning exercise and does not represent the views of any employer, client, or vendor. ServiceNow is referenced solely as an example of a downstream enterprise system.