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, 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 do
A company developed a system that translated natural language questions into API calls, enabling users to easily access data from various sources. This system, initially built on Claude Sonnet 3.5, successfully generated hundreds of reports monthly, becoming a default tool for ad-hoc data retrieval. However, a routine upgrade to Sonnet 4.5 introduced unexpected issues. The new model began incorrectly formatting API requests, sometimes omitting crucial data filters or posing clarifying questions instead of returning structured data.
This situation highlights the critical need for robust testing and monitoring when integrating and upgrading AI models in production systems to prevent service disruptions and ensure data integrity.
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