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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 the architecture landscape.
  • Comparing MCP with custom integrations and API-only approaches.

Designing the Enterprise MCP Architecture

  • Core platform components, interaction flows, and trust boundaries.
  • 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 for reliable interoperability.

Security, Access Control, and Governance

  • Authentication, authorization, and least-privilege design.
  • Data protection, policy enforcement, and auditability.
  • Guardrails for tool usage and sensitive resource access.
  • Governance roles, approval processes, and compliance considerations.

Operations, Deployment, and Adoption Planning

  • Monitoring usage, failures, and platform health.
  • Versioning, lifecycle management, and change control.
  • Cloud, on-premise, and hybrid deployment considerations.
  • 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 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 architecture discussions.

Audience

  • Enterprise architects and solution architects.
  • AI platform architects and technical leads.
  • Stakeholders in integration, security, and governance involved in enterprise AI initiatives.
 7 Hours

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