Building an agentic ML team: multi-agent pipeline with 40% token savings
kmeanskaran · x · 2026-10-02
A developer built a multi-agent ML system where agents scan data and features, generate reports, code like engineers (saving src per run), and ship models directly — with humans only approving train/test splits, model choice and promotion. Prompt and token caching cuts costs by 40%.
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