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observable-autonomy

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AI governance framework built on three architectural principles: Commander's Intent, Observable Autonomy, and Convergence Is Silence. Defines Autonomous Reasoning Fidelity (ARF) as the target property.

  • Updated Jul 2, 2026
  • Python

Persistent memory scaffolding for AI agents - Trail, Destination, Improve, Retrospect, Intent, Probe. Self-built across 221 iterations; declared complete only when GPT, Claude, and Gemini independently found nothing left to change.

  • Updated Jul 2, 2026
  • PowerShell

Real work, fully auditable, in one file: a standalone, target-agnostic improvement-reasoning skill for LLM agents - examines and improves anything the model can reason about (code, documents, plans, letters) while recording every reasoning step in an auditable trail. Single markdown file, no installer, built on the Principles of Earned Autonomy.

  • Updated Jul 5, 2026

Autonomous code improvement loop - harness-captured LLM evidence, operator-held Commander's Intent, CI-enforced. Reference implementation of Principles of Earned Autonomy.

  • Updated Jul 2, 2026
  • Python

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