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Course Outline

Foundations of Model Context Protocol

  • Understanding what MCP is and its role in supporting enterprise AI agent integration
  • Core concepts including clients, servers, tools, resources, and prompts
  • Enterprise use cases and where MCP fits within an architectural landscape
  • Comparing MCP with custom integrations and API-only approaches

Designing the Enterprise MCP Architecture

  • 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 tool exposure, resource access, and request routing
  • Navigating legacy systems, service boundaries, and integration constraints
  • Establishing clear interfaces and contracts for reliable interoperability

Security, Access Control, and Governance

  • Authentication, authorization, and least-privilege design principles
  • Data protection, policy enforcement, and auditability measures
  • 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 platform health metrics
  • Versioning, lifecycle management, and change control procedures
  • Considerations for cloud, on-premise, and hybrid deployments
  • 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 subsequent steps

Requirements

  • Familiarity with enterprise architecture and system integration concepts
  • Understanding of 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 supporting enterprise AI initiatives
 7 Hours

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