This step focuses on the technical backbone that lets AI products scale beyond pilots. You will learn what an AI-ready architecture needs, how MLOps and LLMOps fit into the platform, and how to avoid reinventing infrastructure for every new use case.

Key points

• Use enterprise-grade building blocks where possible. Most organizations should not build GenAI infrastructure from scratch.
• Treat AI like software. DevOps practices still apply, and AI adds extra complexity through data and models.
• MLOps and LLMOps are essential for deploying, monitoring, and improving AI products at scale.
• Multi-agent systems raise the bar for architecture and operations. They are powerful, but complex and hard to test.

Step 3 is about building an architecture that can ship and operate AI repeatedly. When platform choices, operations, and integrations are standardized, new AI use cases stop becoming one-off infrastructure projects.