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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
 7 Hours

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