Robust agentic memory is essential for agents to recall past interactions and preferences, making them more personalized and effective.

Users appreciate how the memory feature enables the AI to understand their context and preferences, enhancing interaction quality, and we're heading towards a world where AI will have incredible context about your life and provide extremely helpful answers.

Having a human in the loop improves results by allowing agents to ask clarifying questions, leading to better outcomes than relying solely on initial prompts.

Feedback mechanisms in AI interfaces can help users refine their prompts and improve interaction with the AI, enhancing the overall experience.

We want users to interact with their AI agents more expressively, potentially allowing them to draw in the UI to communicate changes or ideas.

AI models need to develop a deeper understanding of users beyond single interactions, fostering empathy and contextual awareness to enhance long-term engagement.

Memory management is vital for AI agents; selecting the right memories for tasks can prevent errors and improve performance.

As AI systems evolve, they will need to learn from human interactions to improve personalization and effectiveness, which will be crucial for future applications.

The user experience for ambient agents differs from chat agents, as they operate in the background without direct user interaction, yet it's important for users to engage with them.