MCP Architecture Guide for Enterprise: Connecting Corporate Systems to AI Agents

1. The Enterprise Data Problem

Corporations run HRIS platforms (Workday, SAP SuccessFactors, Oracle HCM, ADP), learning management systems (Cornerstone, Degreed, SAP SuccessFactors Learning, LinkedIn Learning), CRMs (Salesforce, HubSpot, Dynamics 365), ticketing systems (ServiceNow, Jira, Freshdesk), collaboration tools (Teams, Slack, SharePoint), and identity providers (Okta, Azure AD).

Each system has its own API, its own authentication model, its own schema, and its own rate limits. Every workforce automation project — onboarding workflows, compliance dashboards, skills gap analyses — re-solves the integration problem from scratch.

Model Context Protocol (MCP) fixes this. You build one thin wrapper per system (an "MCP server"), add a broker that routes and governs requests, and then any AI agent can ask cross-system questions through a single, stable interface. No more point-to-point integrations.

2. Inventory Your Systems

Before writing any code, document every system that holds data an AI agent might need. For each, note the vendor, what data it holds, and what API is available.

Your inventory will look different. The point is to get every data source on paper before you start building, so you can plan server boundaries and access controls upfront.

3. Build MCP Servers for Each System

Each MCP server wraps exactly one source system. It translates the vendor's API into a set of standardized tools that any MCP-compatible agent can call. One server for Workday. One for Cornerstone. One for ServiceNow. Never combine multiple vendors into a single server — that defeats the purpose.

Here's pseudocode for an HRIS server tool that checks an employee's onboarding status:

@tool("get_employee_onboarding_status")
def get_employee_onboarding_status(employee_id: str) -> dict:
    """
    Pull onboarding checklist status from HRIS.
    Returns completed items, outstanding items, start date,
    department, and assigned manager.
    """
    # Authenticate with scoped service account (read-only)
    client = hris_client(scope="onboarding:read")

    employee = client.get_employee(employee_id)
    checklist = client.get_onboarding_checklist(employee_id)

    return {
        "employee_name": employee.name,
        "start_date": employee.start_date,
        "department": employee.department,
        "manager": employee.manager,
        "checklist_completed": checklist.completed_items,
        "checklist_outstanding": checklist.outstanding_items,
        "checklist_progress": f"{checklist.done}/{checklist.total}"
        # NOTE: No SSN, salary, or benefits data returned
    }

Security defaults for every server

• Scoped service accounts — each server gets its own credentials with minimal permissions. Never share admin tokens.
• Deny-by-default — tools expose only the fields the agent needs. Everything else is excluded.
• PII minimization — employee SSN, salary data, and benefits details are never returned unless the requesting role is explicitly authorized.
• SOC 2 alignment — log every tool call, every response, and every error. Make logs tamper-evident.
• Audit logging — who asked, what tool was called, what data was returned, and when. This is non-negotiable for enterprise deployments.

4. Add an MCP Broker

The broker sits between agents and servers. It handles routing (which server answers which question), policy enforcement (who is allowed to see what), and caching (so you don't hammer source systems with duplicate requests).

Different roles see different data. This is where enterprise RBAC gets enforced:

An employee can ask "What's my PTO balance?" and get an answer. The same employee asking "What's someone else's salary?" gets denied at the broker level before the request ever reaches the HRIS server.

5. Connect Agents

Once your servers are running and the broker is enforcing policy, any MCP-compatible agent can ask cross-system questions. The agent doesn't need to know which system holds which data — it just asks, and the broker routes.

An HR partner asks: "How is this employee's onboarding going?" The agent calls tools on three servers (HRIS, LMS, Ticketing), combines the results, and returns a single coherent answer. No custom integration code. No middleware. No ETL pipeline.

The following examples show what this looks like in practice.

6. Example: Employee Onboarding Status

An HR partner asks the agent: "How is Priya Sharma's onboarding going?"

The agent pulls from three systems:

HRIS (Workday)

• Start date: February 12
• Department: Engineering
• Manager: David Chen
• Onboarding checklist: 6 of 11 items complete
• Outstanding: security training, benefits enrollment, team introduction meeting

LMS (Cornerstone)

• 3 of 5 required onboarding modules complete
• Overdue: "Data Privacy Fundamentals" (due February 17)
• Next up: "Engineering Code Standards"

Ticketing (ServiceNow)

• Laptop: delivered
• VPN access: granted
• GitHub org invitation: pending (submitted February 13, awaiting IT approval)

The agent combines these into a single response:

7. Example: Compliance Training Gaps

A CLO asks the agent: "Which teams have the lowest compliance training completion?"

The agent queries two systems. From HRIS, it pulls all active employees grouped by department. From the LMS, it pulls compliance module completion status for each employee. It cross-references the two, calculates completion rates per team, and identifies the departments below the 80% threshold.

Without MCP, building this report would require a custom integration between the HRIS and LMS, a data pipeline to join records by employee ID, and a separate reporting tool to present the results. With MCP, the agent asks two tools and does the analysis in real time.

8. Example: Knowledge Preservation

When an experienced employee announces retirement, the organization faces an urgent question: what institutional knowledge walks out the door with them?

The agent queries the knowledge base (SharePoint, Confluence) for all documents the employee authored, training materials they created, and processes they own. It cross-references with the LMS to find any courses where they are the sole subject-matter expert. It checks the ticketing system for recurring issues they typically resolve.

This kind of analysis is nearly impossible to do manually across disconnected systems. With MCP servers wrapping each source, the agent can generate this plan in seconds.

9. Security Checklist for Enterprise

Enterprise deployments have non-negotiable security requirements. Every item below should be in place before your MCP infrastructure goes to production.

10. Next Steps

If you're ready to connect your corporate systems to AI agents, here's where to go:

• Talk to our team — we'll walk through your system inventory and scope the project.
• MCP Servers service page — details on how we build, deploy, and manage MCP servers for enterprise clients.
• Enterprise AI solutions — the full picture of what AI agents can do for corporate learning and workforce operations.