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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.
- Core concepts including clients, servers, tools, resources, and prompts.
- Enterprise use cases and the role of MCP within an architecture landscape.
- Comparison of MCP against custom integrations and API-only approaches.
Designing the Enterprise MCP Architecture
- Core platform components, interaction flows, and trust boundaries.
- Evaluating centralized versus distributed integration models.
- Designing 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.
- Designing clear interfaces and contracts to ensure reliable interoperability.
Security, Access Control, and Governance
- Authentication, authorization, and least-privilege design strategies.
- Data protection, policy enforcement, and auditability measures.
- 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 overall platform health.
- 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
- 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.
Requirements
- Knowledge of enterprise architecture and system integration principles.
- 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 in integration, security, and governance involved in enterprise AI initiatives.
