Pain Points:

• Lack of imagination in developing AI agent use cases beyond basic functions like email summarization or auto-replying to messages
• Difficulty thinking creatively about AI agent applications due to technical mindset

Problems to Solve:

• Need for diverse, real-world AI agent use cases beyond basic examples
• Need for inspiration to create more valuable AI agents

Potential Solutions:

• Access to a comprehensive list of real-world AI agent use cases for inspiration
• Community collaboration to expand and refine use case ideas

Pain Points:

• Messy backend APIs with unclear function names
• Outdated documentation causing AI errors
• Agents retrying excessively, frustrating users
• Lack of confirmation steps leading to accidental destructive actions
• Power users feeling out of control without visibility into agent actions
• Infrequent large updates being less effective than smaller iterations

Problems to Solve:

• Improve reliability of AI agents
• Reduce error rates in agent operations
• Prevent user frustration from repetitive retries
• Avoid accidental destructive actions by agents
• Increase user trust and sense of control
• Optimize update frequency for maximum impact

Potential Solutions:

• Standardize API function names clearly (e.g., updateInvoice instead of updateThingFinal)
• Maintain updated OpenAPI documentation
• Implement limited retry mechanisms (2 attempts max)
• Add confirmation steps and undo functionality for critical actions
• Provide 'shadow UI' showing agent's background actions
• Adopt weekly small tweaks instead of quarterly big updates