Parallel Agent Execution with Result Combination (Ensemble Mode)

[DrDejaVuNG]

Problem Statement

When implementing features, different AI models often produce varying implementations with unique strengths. For example, when implementing a feature:

• Some models implement more comprehensive features
• Others produce cleaner, more maintainable code
• Some are more efficient or less redundant
• Others catch edge cases that others miss

Currently, users must manually run tasks with different models and compare results, which is time-consuming and doesn't leverage the collective intelligence of multiple models effectively.

Proposed Solution

Implement a new "Ensemble Mode"(or a better name) that runs multiple AI agents in parallel on the same task, then intelligently combines their results to produce the optimal implementation.

Key Features:

1. Parallel Execution: Spawn multiple agents (different models) to work on the same task simultaneously
2. Isolated Workspaces: Each agent works in a separate git worktree to prevent interference
3. Intelligent Combination: A designated review model analyzes all implementations and combines the best elements
4. Transparent Process: Users can see what was selected from each implementation and why

Detailed Implementation Approach

1. New Mode: "Ensemble Mode"

• Standalone mode
• Accessible via RooCode's mode selection interface
• Configurable: Users can select which models to include in the ensemble

2. Workflow Process

1. User selects Ensemble Mode and specifies task
2. User chooses models (e.g., Claude, Gemini, GPT) and review model (e.g., GPT) (Or done in Settings)
3. System creates separate git worktrees for each agent
4. Agents work in parallel on their assigned worktrees
5. Review model analyzes all completed implementations
6. Review model combines best elements into final implementation
7. Final implementation applied to main branch
8. Clean up temporary worktrees

3. Quality Assessment Criteria

The review model should evaluate implementations based on:

• Code Quality: Readability, maintainability, best practices
• Feature Completeness: Coverage of requirements, edge cases
• Performance: Efficiency, resource usage, etc

4. User Interface Elements

• Model Selection Panel: Choose which models to include in ensemble
• Progress Tracking: Show status of each parallel agent
• Combination Report: Detailed explanation of why certain code was selected

Benefits

1. Superior Code Quality

• Leverages unique strengths of different AI models
• Combines best features from multiple implementations
• Reduces blind spots of individual models

2. Increased Efficiency

• Parallel execution saves time compared to sequential model testing
• Automated combination eliminates manual comparison work
• Faster path to optimal implementation

3. Risk Reduction

• Multiple approaches catch different edge cases
• Redundancy identifies potential issues early
• Cross-validation between models

4. Learning and Transparency

• Users can see different approaches to the same problem
• Understand why certain implementation choices were made
• Learn from the strengths of different models

Question for Discussion

• Should this be a standalone mode or integrated into existing modes?