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

Foundations of Model Context Protocol

• Overview of MCP and its role in supporting enterprise AI agent integration
• Core concepts including clients, servers, tools, resources, and prompts
• Enterprise use cases and MCP's position within the architectural landscape
• Comparison of MCP with custom integrations and API-only approaches

Designing Enterprise MCP Architecture

• Key platform components, interaction flows, and trust boundaries
• Centralized versus distributed integration models
• Design 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
• Addressing legacy systems, service boundaries, and integration constraints
• Crafting clear interfaces and contracts for reliable interoperability

Security, Access Control, and Governance

• Authentication, authorization, and least-privilege design principles
• Data protection, policy enforcement, and audit capabilities
• Guardrails for tool usage and access to sensitive resources
• Governance roles, approval workflows, and compliance considerations

Operations, Deployment, and Adoption Planning

• Monitoring usage, failures, and overall platform health
• Versioning, lifecycle management, and change control processes
• Considerations for cloud, on-premise, and hybrid deployment environments
• Developing a practical rollout roadmap and target operating model

Architecture Workshop

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