Model Context Protocol for Enterprise AI Architects: Designing Secure Agent Integration Platforms Training Course

Foundations of Model Context Protocol

• An overview of what MCP is and how it supports enterprise AI agent integration.
• Core concepts including clients, servers, tools, resources, and prompts.
• Enterprise use cases and where MCP fits within the architecture landscape.
• A comparison of MCP with custom integrations and API-only approaches.

Designing the Enterprise MCP Architecture

• Core platform components, interaction flows, and trust boundaries.
• Centralized versus distributed integration models.
• Strategies for designing reuse, control, and separation of responsibilities.
• Aligning MCP with existing enterprise architecture standards and platforms.

Integration Patterns for Systems and Tools

• Connecting agents to business applications, data services, and internal tools.
• Patterns for tool exposure, resource access, and request routing.
• Handling legacy systems, service boundaries, and integration constraints.
• Designing clear interfaces and contracts to ensure reliable interoperability.

Security, Access Control, and Governance

• Authentication, authorization, and least-privilege design.
• Data protection, policy enforcement, and auditability.
• Establishing guardrails for tool usage and access to sensitive resources.
• Governance roles, approval processes, and compliance considerations.

Operations, Deployment, and Adoption Planning

• Monitoring usage, failures, and platform health.
• Versioning, lifecycle management, and change control.
• Considerations for cloud, on-premise, and hybrid deployment.
• Creating a practical rollout roadmap and defining the target operating model.

Architecture Workshop

• Reviewing a realistic enterprise AI integration scenario.
• Identifying key risks, controls, and architectural decisions.
• Drafting a reference architecture for a secure MCP-based agent platform.
• Presenting design choices and defining next steps.