1. When an agent seeks user guidance to refine its search strategy, it actively involves the user in defining the best approach, improving accuracy by ensuring its search aligns with user expectations.
2. This type of dialogue encourages collaboration, allowing users to clarify ambiguous instructions or adjust the search path as new insights arise.
3. Sharing status updates on task progress is essential for transparency, as it informs users of what the agent has completed and any challenges encountered.
4. Regular updates help users feel informed and give them an opportunity to provide additional instructions if the task requires it.
5. Soliciting user preferences is another valuable dialogue type, where the agent gathers input to shape task outcomes, ensuring decisions align closely with user needs.
6. This approach supports more personalised results, making the task execution feel interactive and responsive to individual preferences.
7. Together, these dialogue types create a flexible, two-way interaction that enhances the quality of task completion by combining automated assistance with user-specific insights.
8. Ultimately, these interactions improve alignment, trust, and satisfaction as the agent works to adapt and optimise its actions based on direct user input.
