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

Foundations of Model Context Protocol

• An 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 the position of MCP within an 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 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 exposing tools, accessing resources, and routing requests
• Addressing 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
• Implementing guardrails for tool usage and sensitive resource access
• Defining governance roles, approval processes, and compliance considerations

Operations, Deployment, and Adoption Planning

• Monitoring usage, failures, and overall platform health
• Managing versions, lifecycles, and change control
• Considering cloud, on-premise, and hybrid deployment options
• 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 next steps