When Claude changed, everything changed: Managing AI blast radius in production

Our system did one thing, and it did it well: It turned natural-language questions into API calls.The users were analysts, account managers, and operations leads. They knew what data they needed, but assembling it manually meant pulling from four dashboards,… Our system did one thing, and it did it well: It turned natural-language questions into API calls.The users were analysts, account managers, and operations leads. They knew what data they needed, but assembling it manually meant pulling from four dashboards, two BI tools, and a Salesforce report builder. With our system, they typed the request in plain English. A request like "Compile a report on sales volume for January through March 2026 for the Northeast region, broken down by city" was translated into an API call that the system could act on:json{  "description": "User requested sales volume for the given date range, here is the API call to get the response",  "api_call": "/api/sales_volume",  "post_body": {    "start_date": "2026-01-01",    "end_date": "2026-03-31",    "region": "northeast"  }}The rest of the pipeline was conventional engineering. The system dispatched the call to the right backend — we had integrations with internal reporting portals, Salesforce, and several homegrown services — applied a large language model (LLM)(-generated JSON query to filter and shape the response, and…