Do you trust your AI Agent?

Explainability is crucial for the deployment of AI agents in real-world applications, as it determines whether these agents earn users' trust and are used for critical tasks. The distinction between "explainability" and "observability" is vital, with the former focusing on understanding the reasoning behind an agent's actions and the latter on recording what happened. The concept of the Explainability stack is introduced, which involves multiple layers ranging from simple outcome verification to deep mechanistic understanding. Each layer caters to different user needs, from basic checks for routine tasks to detailed traces for debugging and compliance. Explainability also varies between synchronous and asynchronous modes, influencing user trust and engagement. Striking the right balance in explainability is essential to avoid overwhelming users with information while ensuring they have enough understanding to trust the agent's autonomy. Ultimately, products with superior explainability are more likely to gain user trust and be chosen for mission-critical tasks, underscoring the importance of explainability as a key differentiator in AI deployment.