Summary

This issue tracks the implementation of parallel execution infrastructure to enable concurrent AI agent workflows in Ralph TUI. The goal is to parallelize work across multiple agents to improve throughput and efficiency.

Motivation

Sequential execution of AI agent tasks creates bottlenecks when agents have independent work that could run concurrently. By introducing parallel execution:

• Increased throughput: Multiple agents can work simultaneously on independent tasks
• Reduced total execution time: Parallel tasks complete faster than sequential runs
• Better resource utilization: CPU cores and I/O bandwidth are used more efficiently
• Improved user experience: Faster feedback loops during development

Scope

The implementation covers:

1. Worktree Pool Manager - Manages git worktrees for isolated parallel workspaces
2. Coordinator Process - Message passing for agent-to-agent communication
3. Resource Lock Manager - Coordinates access to shared resources
4. Task Graph Analyzer - Integration with bd/bv for parallel task identification
5. Parallel Execution Configuration - CLI flags and config options
6. Merge Engine - Consolidates parallel branch changes with backup/rollback
7. AI-Powered Conflict Resolution - Intelligent merge conflict handling
8. Agent Broadcast System - Real-time discovery sharing between agents
9. Parallel Executor - Core executor with continue-on-error semantics
10. Progress Views - Summary view with drill-down and merge conflict UI
11. Rollback Capability - Recovery mechanism for failed merges
12. Agent Spawning Integration - Spawn infrastructure for parallel execution

Related User Stories

• US-010: Progress Summary View with Drill-Down
• US-011: Merge Progress View with Conflict UI
• US-012: Rollback Capability for Merge Recovery
• US-013: Parallel Worktree Session Management Integration
• US-014: Agent Spawning Infrastructure Integration
• US-015: Documentation and Test Suite