AI agents need intent-based chaos testing

Traditional AI testing focuses on happy paths and security, neglecting how autonomous agents behave in unexpected situations. A new approach, intent-based chaos testing, measures deviation from an agent's intended purpose rather than just success rates. This method involves defining behavioral dimensions like tool call accuracy and data access scope, then injecting failures to calculate an 'intent deviation score.' Scores above a certain threshold indicate critical or catastrophic failures, preventing deployment. This rigorous testing, conducted in phases from single tool degradation to composite failures, aims to catch costly errors before they impact production systems, addressing a critical gap in current AI development practices.