A common frustration occurs when users ask AI assistants to perform tasks, like booking a flight, but the assistant fails to handle subsequent requests, resulting in a poor experience.

Managing user expectations is challenging, especially when AI delivers results that may not fully meet their needs, necessitating manual features to enhance user experience.

Creating a feature-complete AI agent simplifies the user experience and reduces frustration by supporting all tools simultaneously rather than one at a time.

The disconnect between a well-developed frontend experience and the slower development of AI tools leads to user confusion about which features are supported.

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.

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.

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 confusion around agents stems from the expectation that AI will develop human-like intent and creativity, which may not be theoretically possible.

The integration of AI agents into consumer services is complicated by data silos and companies' reluctance to provide automated access to their services, which could limit agent deployment.