Boosting article engagement with multi-agent AI

A large media company needed a scalable way to understand reader intent and surface engaging questions for each article. Manual question creation was slow, inconsistent, and couldn't adapt to changing user behavior — resulting in missed engagement opportunities across thousands of daily articles.

We built a multi-agent system where specialized agents identify user intent, generate three candidate questions per article, and route them through a multi-armed bandit with reinforcement learning. The system continuously optimizes which questions maximize click-through rates, learning from real reader behavior in real time. Each agent has a defined role: intent classification, question generation, and performance optimization.

Article click-through rates increased by 40%. The editorial team was freed from manual question creation, and the system continuously improves without human intervention. The architecture is now being extended to other content formats.