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The agent harness shouldn't be assembled per team from loose configs. Here's why platform teams should deliver it as a versioned, catalogued capability.

Why AI rollouts stall isn't the model; it's platform orchestration. See how enterprises govern AI agents at scale without slowing delivery down.

Giving every team direct Kubernetes API access to build custom controllers creates a noisy neighbour problem. Here's the abstraction that avoids it.

LLMOps keeps large language models reliable in production. Learn what it involves, how it differs from MLOps, and why platform engineering is where it belongs.

I recently gave a talk called "Your Platform Team Is the Bottleneck: Operating Internal Platforms at Enterprise Scale," and the title wasn't meant to be provocative for its own sake. It's just what I keep seeing. I've been a platform engineer, and I've owned platform teams, and the pattern repeats itself almost every time an organisation grows past a certain size: the team that was supposed to remove friction becomes the place where everything gets stuck. That's not a failure