How do you implement agentic AI for automated product recommendation engines?
Product recommendation agents analyze user behavior, purchase history, and item attributes to generate personalized recommendations. Configure agents with access to product catalogs, user interaction data, and inventory systems. Implement multiple recommendation strategies including collaborative filtering based on similar users, content-based filtering using product attributes, and hybrid approaches. The agent contextualizes recommendations based on session behavior, cart contents, and seasonal trends. Support real-time recommendation updates as user behavior changes within a session. Implement recommendation diversity to avoid filter bubbles. A/B test recommendation algorithms to optimize conversion. Track recommendation click-through rates, conversion rates, and revenue attribution per strategy.