When using AI for coding, there is often a gap between the code generated by AI and the user's ability to modify it effectively, highlighting the need for better tools.

AI agent teams often lag behind frontend teams, resulting in a struggle to keep up with new features and tools that enhance user experience. However, building an AI agent interface is straightforward if you leverage existing features rather than starting from scratch, allowing for broader access to tools and services.

The biggest concern for product managers regarding AI agents is cost, as many platforms price based on usage rather than value.

While AI can build applications faster than humans, it still requires human oversight to ensure quality and creativity in the development process.

We need to keep AI on a leash, as over-reliance on AI agents can lead to issues like introducing bugs or security vulnerabilities.

There's a lot of excitement around AI agents, but many people are unsure of their practical applications, often trying to apply them to problems that simpler solutions could address.

The integration of AI into software development raises questions about managing non-deterministic behavior and the reliability of AI-driven applications.

One overhyped aspect of AI agents is their perceived capabilities, while the underhyped aspects include the importance of understanding when and how to use them effectively in production, as well as how they can save people time by automating tasks that take only a minute, significantly changing dynamics and allowing you to perform those tasks many more times than before.

Most AI applications today, particularly those labeled as AI agents, often have low operational costs due to their reliance on software and LLMs, which are becoming cheaper over time.