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Course Outline

Foundations of Model Context Protocol

  • Overview of MCP and its role in supporting enterprise AI agent integration
  • Core concepts including clients, servers, tools, resources, and prompts
  • Enterprise use cases and MCP's position within the architectural landscape
  • Comparison of MCP with custom integrations and API-only approaches

Designing Enterprise MCP Architecture

  • Key platform components, interaction flows, and trust boundaries
  • Centralized versus distributed integration models
  • Design 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
  • Addressing legacy systems, service boundaries, and integration constraints
  • Crafting clear interfaces and contracts for reliable interoperability

Security, Access Control, and Governance

  • Authentication, authorization, and least-privilege design principles
  • Data protection, policy enforcement, and audit capabilities
  • Guardrails for tool usage and access to sensitive resources
  • Governance roles, approval workflows, and compliance considerations

Operations, Deployment, and Adoption Planning

  • Monitoring usage, failures, and overall platform health
  • Versioning, lifecycle management, and change control processes
  • Considerations for cloud, on-premise, and hybrid deployment environments
  • Developing a practical rollout roadmap and target operating model

Architecture Workshop

  • Analysis of a realistic enterprise AI integration scenario
  • Identification of key risks, controls, and architectural decisions
  • Drafting a reference architecture for a secure MCP-based agent platform
  • Presenting design choices and defining subsequent steps

Requirements

  • Knowledge of enterprise architecture and system integration concepts
  • Familiarity with APIs, cloud or on-premise platforms, and foundational security controls
  • Experience in technical solution design or architectural discussions

Target Audience

  • Enterprise and solution architects
  • AI platform architects and technical leads
  • Integration, security, and governance stakeholders involved in enterprise AI initiatives
 7 Hours

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