If your AI agent makes a bad decision, how would you trace back to understand why?

One of the biggest challenges in building AI is understanding why it does what it does, especially when it makes a mistake. This is called 'explainability.' If an AI agent recommends the wrong movie, it's not a big deal. But if it makes a critical decision in medicine or finance, understanding its logic is crucial. Thinking about this helps you design agents that are easier to understand and fix, building trust in the technology.