Implementation of Human Intervention Abstraction in RePlan

[matiasmolinas]

Is your feature request related to a problem? Please describe.
RePlan currently lacks a robust abstraction for handling human interventions, such as validation, correction, clarification, and decision-making during workflows. This limitation reduces the framework's adaptability and effectiveness in managing complex scenarios that require user input to resolve ambiguities, choose between multiple solutions, or refine plans. Without this feature, workflows may proceed with incorrect assumptions or suboptimal decisions, impacting accuracy and outcomes.

Describe the solution you'd like
To address this, I propose implementing a reusable Intervention Class to manage human intervention workflows. This class will support:

1. Validation (Full Support):

   • The agent validates steps and parameters before execution.
   • Example: The agent confirms if the selected step or tool parameters are correct.

2. Correction (Basic Support):

   • The agent identifies errors and requests user input for correction.
   • Example: The agent prompts the user to correct a project deadline.

3. Clarification (Basic Support):

   • The agent resolves ambiguous inputs by asking for additional details.
   • Example: The agent clarifies unspecified input, such as the target language for a translation.

4. Decision-Making for Multiple Solutions:

   • The agent presents multiple paths or solutions and allows the user to decide or refine the plan.
   • Example: The agent suggests multiple venue options for an event based on conflicting requirements and prompts the user to choose or rank preferences.

The Intervention Class will:

• Provide a unified interface for handling validation, correction, clarification, and decision-making workflows.
• Dynamically enable or disable intervention features using runtime flags.

Describe alternatives you’ve considered

1. Ad-hoc Interventions:

   • Handling validation, correction, clarification, and decision-making independently in each workflow.
   • This approach lacks scalability and consistency across workflows.

2. Fully Automated Workflows:

   • Attempting to handle all scenarios with automation only.
   • This reduces flexibility and increases the risk of errors or suboptimal decisions in nuanced or high-stakes situations.

Additional context
Planned Implementation:

1. Intervention Class:

   • A reusable class to standardize workflows for validation, correction, clarification, and decision-making.
   • Supports runtime flags for dynamic enabling/disabling of intervention features.

2. Validation (Full Support):

   • Ensures steps and tool parameters are validated before execution.
   • Example Scenarios:
     • Step Validation: Agent Prompt: "I am about to perform the step 'Optimize Database Queries'. Is this correct?"
     • Parameter Validation: Agent Prompt: "I will call the WeatherTool with 'Santa Fe, Argentina'. Please confirm if this is correct."

3. Correction (Basic Support):

   • Handles simple corrections based on user input.
   • Example Scenarios:
     • Agent Prompt: "The expected value is 42, but it was set to 24. Should I correct it?"
     • Agent Prompt: "There seems to be an error in the project deadline. Please provide the correct date."

4. Clarification (Basic Support):

   • Requests additional details for ambiguous inputs.
   • Example Scenarios:
     • Agent Prompt: "Could you clarify what you mean by 'optimize the system'?"
     • Agent Prompt: "You requested a translation but didn’t specify the target language. Could you clarify?"

5. Decision-Making for Multiple Solutions:

   • Supports scenarios where more than one solution is possible, and the user selects or refines the best path.
   • Example Scenarios:
     • Event Planning: The agent detects conflicting requirements and presents venue options based on attendee count, location, and cost. Agent Prompt: "Here are three venue options based on your requirements. Which one would you prefer?"
     • Financial Planning: The agent retrieves multiple strategies for optimizing tax compliance across regions. Agent Prompt: "These are the top three strategies for tax compliance. Please choose one or refine the approach."

6. Runtime Flag for Human Intervention:

   • Allows dynamic toggling of human intervention features to ensure flexibility in different scenarios.

This implementation will significantly enhance RePlan’s adaptability and accuracy, allowing it to tackle complex workflows with greater confidence and reliability. By integrating user input into critical decision points, RePlan will provide a more interactive and user-centric experience.

[matiasmolinas]

Hi @mmurad2

Thank you for your feedback and guidance on the related issue #121. I’ve created this new issue to continue the work on implementing human intervention, including validation, correction, clarification, and decision-making scenarios.

Could you please assign this issue to me so I can proceed with the implementation? Let me know if there are any additional requirements or considerations to keep in mind.

Thanks!