Starting simple and having measurable results is crucial as you build complexity into your AI agents. This approach allows you to adapt and improve as you learn what works.

Quality management is vital for AI agents, involving metrics analysis and deep feedback on user interactions to continuously improve performance and address any issues that arise during deployment.

Companies are increasingly focused on the business value of AI agents rather than just technical benchmarks, emphasizing the importance of measurable impact.

Many AI agent assistants fail because they are built with incomplete features, leading to frustrating user experiences.

AI agents may evolve to close feedback loops without human intervention, but they currently lack the instinct to recognize and correct their own errors, which is a significant barrier to their effectiveness.

The challenge lies in defining the right measures of progress in AI evaluation, moving beyond traditional benchmarks.

The challenge lies in defining the right measures of progress in AI evaluation, moving beyond traditional benchmarks.

When AI gives mediocre results, treating it as a teammate means providing feedback and coaching to improve its output.

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.