Inspecting metrics like adoption, engagement, and retention is essential to understand how users interact with an AI product and its effectiveness.

Key metrics for evaluating AI agents include deflection rate, which measures how many conversations the AI can handle without human escalation, and customer satisfaction scores, which reflect user experience.

To effectively build AI agents, ensure you have a way to measure results. Many developers build in a vacuum without feedback, which can lead to ineffective solutions.

The iterative process of deploying AI systems, gathering feedback, and learning is crucial for building safe systems as we advance into more complex AI.

The development of AI agents involves creating systems that can autonomously handle tasks and communicate issues back to users for resolution.

Testing AI agents involves pre-deployment testing with curated conversation sets to ensure expected responses, followed by ongoing evaluation to adapt to changing user interactions and questions.

The admin dashboard is crucial for customers to build and configure their AI agents, allowing them to test and roll out the agent safely while monitoring performance and user interactions.

The ability to prompt AI effectively is crucial, as it determines the quality of the output, which is a reflection of agency and taste in AI development.

Testing is essential for ensuring that AI systems behave as expected, not just in terms of performance but also in their underlying behaviors.