Ben's session

The author details a frustrating experience using an AI agent to create a Google Calendar booking link, highlighting inefficiencies in their own prompting and guidance. Despite the agent eventually succeeding in building a functional Chrome extension, the process involved significant debugging loops and model-switching due to issues with context limits and testing protocols. The core takeaway is that the agent’s capability is less important than the user’s skill in providing clear, iterative instructions and context for the AI.

• The user attempted to use an AI agent to create a Google Calendar booking link, wanting to drag time slots instead of manually entering them.
• Initial prompting was insufficient, leading to the agent suggesting an extension that required manual syncing rather than automatic updates.
• The user discovered the importance of clear, iterative instructions, including telling the agent to install, test, and iterate live within the browser.
• Context window limitations and the agent’s approach to compaction caused repeated relearning of information, hindering debugging.
• Switching to a more powerful reasoning model (Sol) was necessary for effective iterative debugging.
• The author emphasizes that the agent’s capability is high, but user guidance in defining tasks, verification criteria, and managing context is crucial for efficiency.
  https://bender.layer3.press/articles/9fea00fb-500a-47ec-9622-dae86eb218e4