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Module 15: AI Infrastructure & Future Systems Slides

Slide walkthrough for Module 15 of Production-Grade RAG Systems Engineering: MCP architecture, AI runtime systems, agent platforms, and workload identity...

This slide page is the visual review companion for the full course module. Use it to recap the architecture, examples, exercises, production warnings, and takeaways after reading the lesson.

Slide Outline

  1. AI Infrastructure & Future Systems - MCP architecture, AI runtime systems, agent platforms, and workload identity for AI
  2. Learning Objectives - 4 outcomes for this module
  3. Why This Module Matters - AI infrastructure is the fastest-evolving area of platform engineering. Engineers who understand how to secure, deploy,
  4. MCP: Model Context Protocol - Lesson section from the full module
  5. AI Agent Identity - Lesson section from the full module
  6. Building Future-Proof AI Infrastructure - Lesson section from the full module
  7. Hands-On Labs - 2 hands-on labs
  8. Key Takeaways - 5 points to remember

Learning Objectives

  • Understand MCP (Model Context Protocol) architecture
  • Design AI runtime systems for production
  • Secure AI agents with workload identity (SPIFFE)
  • Build future-proof AI infrastructure

Why This Module Matters

AI infrastructure is the fastest-evolving area of platform engineering. Engineers who understand how to secure, deploy, and operate AI systems — not just build chatbots — are positioned for the most in-demand roles in the industry.

Key Takeaways

  • MCP standardizes how agents access tools — like HTTP for AI-tool communication
  • AI agents need cryptographic identity (SPIFFE) not shared API keys
  • OPA policies control what each agent can access based on its identity
  • Production AI infrastructure needs: identity + encryption + authorization + observability
  • These concepts connect directly to SPIFFE/SPIRE and Cloud Native Security courses

Hands-On Labs

  1. MCP Server Integration

    Connect your RAG system to MCP servers for tool access.

    35 min - Advanced

    • Build a simple MCP server exposing document search
    • Connect an AI agent to the MCP server
    • Test tool discovery and execution
    • Add authentication between agent and server

    View lab files on GitHub

  2. Secure AI Agents with Identity

    Give AI agents SPIFFE identity and OPA policies.

    35 min - Advanced

    • Deploy SPIRE and register AI agent workloads
    • Configure mTLS between agent and services
    • Add OPA policy controlling per-agent access
    • Audit agent tool usage with verified identity

    View lab files on GitHub

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