How to debug failures or missteps in AI agent behavior?

Debugging AI agents involves understanding the unique challenges posed by their potential to hallucinate, make incorrect decisions, or ignore instructions despite apparently successful executions. Unlike traditional software debugging, AI debugging requires examining the agent's decision-making process to determine what actions were taken and why. The process can be broken down into three levels: tagging and filtering executions to quickly identify problematic runs, tracing the decision chain to understand the sequence of actions, and tuning model parameters or switching models if necessary. In practice, most failures are attributed to issues with context, such as missing data or ambiguous tool descriptions, rather than inherent model limitations. Effective debugging requires not only addressing immediate issues but also establishing evaluation processes to prevent recurring failures. Tools like n8n facilitate this process by offering execution data tagging, detailed trace inspections, and integration with external platforms for comprehensive debugging and evaluation, ultimately aiming to make failures diagnosable and improve agent reliability over time.