Quantum-resistant, self-learning version control for AI agents with ReasoningBank intelligence and multi-agent coordination
Tasks
Track a feature implementation by starting a ReasoningBank trajectory, committing changes, adding operations to the trajectory, and finalizing it with a success score and critique
Retrieve AI suggestions for a specific task by querying past trajectories and checking the returned confidence and expected success rate
Review learned operational patterns by calling getPatterns() to identify successful operation sequences
Generate and verify a SHA3-512 quantum fingerprint for a buffer of data using the built-in cryptography methods
Inputs
Task descriptions for learning trajectories (non-empty strings, maximum 10,000 bytes)
Success scores between 0.0 and 1.0 for trajectory finalization
Base64-encoded encryption keys for enabling HQC-128 trajectory encryption
String queries for searching similar trajectories
Outputs
JjResult objects from version control operations like status, newCommit, and branchCreate
JSON strings containing DecisionSuggestion objects with confidence, expectedSuccessRate, and reasoning
JSON strings containing LearningStats with totalTrajectories, totalPatterns, and improvementRate
64-byte Buffer containing a SHA3-512 quantum fingerprint
Limitations and checks
Low confidence suggestions may occur if fewer than 5-10 trajectories have been recorded
Success scores must be finite numbers between 0.0 and 1.0, rejecting NaN or Infinity
Trajectories require at least one tracked operation before they can be finalized
Task descriptions cannot be empty or whitespace-only and are automatically trimmed
Ensure trajectory task descriptions are meaningful, non-empty strings under 10,000 bytes and not vaguely named like 'fix stuff'
Verify that success scores passed to finalizeTrajectory accurately reflect the outcome rather than always defaulting to 1.0
Confirm that trajectory failures include detailed critiques explaining the root cause to enable future learning
Check the confidence value of getSuggestion output and ensure sufficient trajectory data exists before relying on recommendations
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