Event-Driven AI Agents: Patterns That Scale

Most AI agent tutorials teach you to build a chatbot that waits for user input. But production agents do not wait -- they react. A deploy finishes and your agent runs smoke tests. A customer signs up and your agent sends a personalized onboarding sequence. A monitoring threshold trips and your agent pages the on-call engineer before a human even notices. The architecture that makes this possible is event-driven design. And getting it right is the difference between agents that demo well and agents that run your operations. This guide covers four event-driven architecture patterns for AI agents, each with runnable Python code you can adapt today. No vendor lock-in, no Kafka required, no enterprise sales pitch -- just patterns that work. Why Polling Fails for Production Agents Before diving into patterns, let's be clear about why the default approach breaks down. Polling is the naive solution: your agent checks a database, API, or inbox on a timer. "Any new emails? No? Check again in 30