Beyond the Happy Path: Why AI Agents Need Adversity Testing

AI agents work seamlessly in controlled environments but crumble under production's real-world complexity—transient errors, model shortcuts, and validation failures that users will certainly encounter. Without rigorous stress testing frameworks that simulate chaos and validate actual outputs, even well-designed agents become unreliable in production. This shift toward adversity-driven testing represents a critical evolution from prototype development to production-grade reliability.