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

Foundations of Model Context Protocol

• Understanding what MCP is and its role in supporting enterprise AI agent integration
• Core concepts including clients, servers, tools, resources, and prompts
• Enterprise use cases and where MCP fits within an architectural landscape
• Comparing MCP with custom integrations and API-only approaches

Designing the Enterprise MCP Architecture

• Platform components, interaction flows, and trust boundaries
• Centralized versus distributed integration models
• Strategies for 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
• Navigating legacy systems, service boundaries, and integration constraints
• Establishing clear interfaces and contracts for reliable interoperability

Security, Access Control, and Governance

• Authentication, authorization, and least-privilege design principles
• Data protection, policy enforcement, and auditability measures
• 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 metrics
• Versioning, lifecycle management, and change control procedures
• Considerations for cloud, on-premise, and hybrid deployments
• Developing a practical rollout roadmap and target operating model

Architecture Workshop

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