Agentic AI demos often look like magic — agents planning, reasoning, and solving tasks on the fly. But beneath the surface, the reality is clear: building agentic systems is ninety percent engineering.

In this episode, we unpack what that really means. From planning and memory to tool use, orchestration, and monitoring, the hardest challenges are not in the model itself, but in the systems built around it. Just as cloud adoption matured through disciplines like DevOps and FinOps, Agentic AI will demand its own engineering maturity.

I believe a surge is coming — where the real differentiator will not be who uses the biggest model, but who engineers the most reliable, efficient, and sustainable systems.

The magic of Agentic AI lies not in the demo, but in the discipline of the build.