Using Classical ML to Empower AI Agents
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What the video says
Why are cutting-edge AI agents suddenly relying on old-school machine learning? Modern agentic platforms are reaching an inflection point.
They are quietly integrating classical ML models like CatBoost and XGBoost as core tools. LLMs excel at translating human prompts, but they are notoriously bad at calculating precise numbers.
These massive models guess rather than perform empirical calculations. This creates a historic risk for accuracy and interpretability.
Classical classifiers solve this problem. They are incredibly lightweight and highly predictable.
Running them avoids volatile LLM token costs. It also keeps critical data under your direct control.
Relying solely on third-party LLMs introduces massive risks of infrastructure downtime. Practitioner Stephanie Kermer argues that agentic AI needs classical ML much more than previously thought.
Combining both worlds is a step change that delivers the precision and cost efficiency these flagship systems actually require.
