A new tutorial from Machine Learning Mastery walks through the process of integrating agentic AI with an existing classical machine learning pipeline. Rather than treating the two approaches as competitors, the author frames them as complementary: the classical pipeline continues to handle structured prediction tasks, while an agentic AI layer takes on the open-ended reasoning and action-selection work that traditional models are not designed for.
The concrete example is a customer-service setting. The classical pipeline might classify or route a request, but the agentic system can interpret a free-text message, decide what follow-up action is needed, and orchestrate the response. The tutorial positions this as a hybrid architecture that makes an existing ML pipeline more autonomous without requiring a full rewrite.
Because there is only one source, there is no second perspective to compare against. The article is prescriptive and practical, focused on how to build rather than on broader risks or trade-offs of agentic systems. Readers should treat it as a how-to guide for one specific integration pattern, not as a survey of approaches.