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Upcoming Features

This document outlines planned improvements and features for Airtrain, with a special focus on AI Employee capabilities.

AI Employee Enhancements​

Interrupt Handling​

AI Employees will be able to:

  • Handle interruptions gracefully during long-running tasks
  • Save and restore context when interrupted
  • Prioritize between current task and interruption
  • Resume tasks from saved state
class AIEmployee(BaseAIEmployee):
    async def handle_interrupt(self, interrupt_context):
        # Save current context
        await self.save_current_state()
        
        # Handle interrupt
        await self.process_interrupt(interrupt_context)
        
        # Resume previous task
        await self.restore_state()

Long-Running Task Management​

Support for extended operations with:

  • Progress tracking
  • Intermediate state saving
  • Checkpointing
  • Resource management over time
  • Task suspension and resumption

Dynamic Memory Management​

Natural language-based memory control:

# Example of natural language memory management
employee.update_memory_strategy("""
    Remember all customer interactions related to billing issues
    Keep track of pending approvals for the next month
    Forget temporary calculation results after each task
""")

Dynamic Permission Management​

Natural language permission updates:

  • Request new permissions as needed
  • Explain why permissions are needed
  • Suggest permission removals when no longer needed
  • Handle permission changes during runtime

Example interaction:

Employee: "I need access to the billing system to help this customer. Should I request this permission?"
User: "Yes, but only for today"
Employee: "Thank you. I'll request temporary billing system access and automatically remove it at the end of the day."

Contextual Memory Storage​

Subjective memory management based on user instructions:

class ContextualMemory:
    async def store_by_instruction(self, instruction: str, content: Any):
        """
        Store content based on natural language instruction
        
        Example:
        'Remember this for all future customer service interactions'
        'Keep this only for today's tasks'
        'This is important for the quarterly review'
        """
        memory_context = await self.analyze_instruction(instruction)
        await self.store_with_context(content, memory_context)

Planned Features​

1. Advanced Memory Management​

  • Hierarchical memory organization
  • Context-based memory retrieval
  • Memory importance scoring
  • Automatic memory consolidation
  • Memory lifetime management

2. Natural Language Control​

  • Task modification through conversation
  • Permission management via dialogue
  • Memory management through instructions
  • Priority adjustment through discussion

3. Adaptive Behavior​

  • Learning from user interactions
  • Adjusting communication style
  • Improving task efficiency
  • Optimizing resource usage

4. Enhanced Interruption System​

  • Priority-based interrupt handling
  • Context preservation
  • Multi-level interrupt queues
  • Graceful task suspension

5. Improved User Interaction​

  • Natural dialogue for permissions
  • Contextual help suggestions
  • Proactive resource management
  • Clear explanation of actions

Implementation Timeline​

  1. Q3 2024

    • Basic interrupt handling
    • Simple natural language memory management
    • Initial dynamic permission system

  2. Q4 2024

    • Advanced memory management
    • Enhanced interrupt handling
    • Improved natural language control

  3. Q1 2025

    • Full contextual memory system
    • Complete permission dialogue system
    • Advanced task management

Feedback and Suggestions​

We welcome community input on these planned features. Please:

  • Share your use cases
  • Suggest additional features
  • Provide implementation feedback
  • Report specific requirements

Next Steps​

  • Join our discussion forum
  • Watch our GitHub repository
  • Sign up for beta testing
  • Contribute to feature development