Before, during, after: The three moments AI Agents earn your trust

As AI capabilities have advanced, the focus has shifted from questioning their potential to trusting their outcomes, which necessitates integrating explainability directly into AI workflows. Explainability is crucial for verification, debugging, and auditability, and must be present at three key stages: before, during, and after the AI's work. Before the work, AI should clarify its understanding and approach to tasks; during the work, it should reveal its decision-making process; and after the work, it should comprehensively explain the impact of changes, including those not immediately visible. As AI systems become more autonomous, the human role is moving towards oversight and validation, making explainability a core component, rather than a supplementary feature, in AI tools. This approach allows humans to effectively manage AI output without reviewing every detail, by providing actionable insights at critical decision points.

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