AI infrastructure is splitting between tools for building apps and tools for running them safely in production.

Engineering and product teams are moving from demos to production LLM features, and the work gets harder once users depend on the output. The buyer is the team shipping AI into an app, not the person casually testing a chatbot.

The work includes testing prompts before release, tracking versions, running evals, giving coding agents safe cloud sandboxes, orchestrating multiple agents, and monitoring models in production. It matters now because failures are quieter than normal software bugs: the feature can still run while the answer gets worse.