Anyone today can build an agent locally with minimal effort. With some LLM calls, a prompt, and a few tool definitions, that agent will be doing real work for them within minutes. But what happens when that agent needs to get into production and be used by the entire engineering department, with real data and real consequences?

In 2015, Google published “Hidden Technical Debt in Machine Learning Systems.” That paper turned on the light for machine learning engineers and named all the problems they were experiencing. The diagram…

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