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Duration 7 hours
Course Outline
Foundations of the Model Context Protocol
- Defining MCP and its role in facilitating enterprise AI agent integration.
- Key components including clients, servers, tools, resources, and prompts.
- Identifying enterprise use cases and positioning MCP within the broader architecture landscape.
- Comparing MCP against custom integrations and purely API-driven approaches.
Architecting Enterprise MCP Solutions
- Defining core platform elements, interaction flows, and trust boundaries.
- Evaluating centralized versus distributed integration models.
- Designing for reusability, control, and clear separation of duties.
- Aligning MCP implementations with existing enterprise architecture standards and platforms.
Integration Patterns for Systems and Tools
- Linking agents to business applications, data services, and internal utilities.
- Establishing patterns for tool exposure, resource access, and request routing.
- Managing legacy systems, service boundaries, and integration constraints.
- Crafting clear interfaces and contracts to ensure reliable interoperability.
Security, Access Control, and Governance
- Implementing authentication, authorization, and least-privilege principles.
- Ensuring data protection, policy enforcement, and full auditability.
- Setting guardrails for tool usage and access to sensitive resources.
- Defining governance roles, approval workflows, and compliance requirements.
Operations, Deployment, and Adoption Strategy
- Monitoring usage metrics, failure rates, and overall platform health.
- Managing versioning, lifecycle, and change control processes.
- Considering cloud, on-premise, and hybrid deployment models.
- Developing a practical rollout roadmap and target operating model.
Architecture Workshop
- Analyzing a realistic enterprise AI integration scenario.
- Identifying critical risks, controls, and architectural decisions.
- Drafting a reference architecture for a secure MCP-based agent platform.
- Presenting design rationales and outlining subsequent action steps.
Requirements
- A solid grasp of enterprise architecture principles and system integration fundamentals.
- Proficiency with APIs, cloud or on-premise platforms, and foundational security controls.
- Background in designing technical solutions or participating in architectural discussions.
Target Audience
- Enterprise and Solution Architects.
- AI Platform Architects and Technical Leads.
- Stakeholders in integration, security, and governance involved in enterprise AI projects.
