AI
Using Classical ML to Empower AI Agents
In an article for Towards Data Science, Stephanie Kirmer highlights how agentic AI platforms are increasingly relying on classical machine learning models
Key takeaways
- Agentic AI needs classical ML much more than previously thought to handle tasks where LLMs struggle.
- LLMs are particularly bad at calculating meaningful numbers because they guess rather than make empirical calculations.
- Running classical classifiers or regression models is incredibly lightweight and cheap compared to unpredictable LLM token costs.
- Using third-party LLMs creates risks regarding data control and infrastructure downtime.
In an article for Towards Data Science, Stephanie Kirmer highlights how agentic AI platforms are increasingly relying on classical machine learning models (like regression and classification models) rather than relying solely on LLMs. While LLMs excel at translating human prompts and orchestrating workflows, they are poorly suited for precise calculations, lack interpretability, and introduce higher costs and data privacy risks. Integrating classical ML tools - either through direct API calls or pre-calculated database queries - provides AI agents with superior accuracy, precision, cost efficiency, and infrastructure control.
In their words
“agentic AI needs classical ML much more than we probably thought.”
How it unfolded
- Author was concerned about the future of classical ML.
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Common questions
- What happened with Stephanie Kirmer?
- Running classical classifiers or regression models is incredibly lightweight and cheap compared to unpredictable LLM token costs.
- Where can I read the original report?
- Read the full report at towards_data_science.
