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Duration 7 hours
Course Outline
Foundations of Model Context Protocol
- Understanding what MCP is and how it facilitates enterprise AI agent integration
- Key concepts including clients, servers, tools, resources, and prompts
- Enterprise use cases and the role of MCP within the architectural landscape
- Comparing MCP with custom integrations and API-only approaches
Designing Enterprise MCP Architecture
- Core platform components, interaction flows, and trust boundaries
- Evaluating centralized versus distributed integration models
- Architecting 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
- Managing legacy systems, service boundaries, and integration constraints
- Establishing clear interfaces and contracts to ensure reliable interoperability
Security, Access Control, and Governance
- Implementing authentication, authorization, and least-privilege design
- Ensuring data protection, policy enforcement, and auditability
- Setting guardrails for tool usage and sensitive resource access
- Defining governance roles, approval processes, and compliance considerations
Operations, Deployment, and Adoption Planning
- Monitoring platform health, failures, and usage metrics
- Managing versioning, lifecycle, and change control
- Considering cloud, on-premise, and hybrid deployment options
- Developing a practical rollout roadmap and target operating model
Architecture Workshop
- Examining 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 outlining next steps
Requirements
- Knowledge of enterprise architecture and system integration concepts
- Familiarity with APIs, cloud or on-premise platforms, and fundamental security controls
- Experience in technical solution design or architectural discussions
Audience
- Enterprise architects and solution architects
- AI platform architects and technical leads
- Stakeholders involved in enterprise AI initiatives, particularly those focusing on integration, security, and governance
