Large Language Models and AI Agents… the Easy Way | Nov, 2025
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When your supply techniques change in a single day, conventional information workflows break. Right here’s how I constructed an AI agent development solution to deal with schema evolution, and the surprising benchmark outcomes between Claude, GPT, and Gemini.
Finally Tuesday at 2:47 AM, our Salesforce occasion added 14 new customized fields. By 8:30 AM, three analysts had been asking why their dashboards were regarded as “bizarre.” By midday, our VP of Gross sales was in my workplace asking if we’d “misplaced information.”
We hadn’t misplaced something. The pipeline simply didn’t know what to do with the brand new fields.
That is the invisible tax of contemporary information groups: schema drift. Sources change always — new fields seem, information varieties shift, naming conventions evolve. And every time, somebody has to manually replace ingestion configs, transformation logic, and downstream marts.
After the fifth hearth drill in two months, I made a decision to construct one thing totally different: an AI agent builder designed for automated data management and AI workflow automation.
The Drawback With Conventional Change Administration
Most information groups deal with schema modifications the identical manner:
Supply system modifications (normally…)
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