agentic-jujutsu

Multi-agent version control coordinates concurrent AI coding, review, and deployment operations by applying trajectory tracking and shared memory to prevent conflicts and preserve learning from previous operations.

Self-learning trajectories track tasks, operations, outcomes, and critiques so future agent runs can improve from past experience by retrieving recommended actions and success patterns.

Multi-agent coordination prevents conflicts during simultaneous branching, merging, and deployment by using lock-free workflows with operation logging, validation checks, and shared memory.

Quantum-resistant integrity support provides optional encryption and secure fingerprints for faster verification of protected trajectories in multi-agent version control workflows.

Recording outcomes requires starting a trajectory for a coding task, logging operations performed, and finalizing it with a success score and critique for retrieval by future agents.

Multi-agent coordination supports concurrent branching, committing, and reviewing without relying on lock-heavy workflows by using shared memory and outcome scoring to manage concurrent operations.