How to Combine Traditional Machine Learning with Agentic Reasoning

Traditional machine learning excels at fast, reliable pattern recognition on structured data but cannot plan, adapt mid-task, or take actions in the world. Agentic reasoning, built around LLMs as reasoning engines, fills that gap by adding planning, tool use, adaptation, and action. Hybrid systems combine the two: agents orchestrate multi-step workflows while trained models handle specialized prediction, illustrated through an insurance claims fraud detection example where an agent invokes a fraud model, gathers context, and routes claims with a documented reasoning trace.

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