dev@bfd:~/dev-diary$ git show 2026-01
commit 2026-01-17-ai-sentinel-audit-and-repair
Author: MJ
Date: Jan 17, 2026

2026-01-17 - AI Sentinel Audit and Repair

Timeline

  • (Context) Initiated comprehensive audit of AI Sentinel system to investigate inconsistent analysis depths.
  • (Observation) Identified critical breakage in Stage 2 output format where prose was replacing required checkboxes.
  • (Observation) Noticed missing “Golden Examples” directory was causing agent hallucination regarding output structure.
  • (Action) Created three Golden Example files to anchor agent behavior for seeds and analyses.
  • (Action) Updated Stage 2 prompt to strictly enforce checkbox formatting with self-validation steps.
  • (Action) Updated Stage 3 prompt to mandate Knowledge Base visibility in final outputs.
  • (Action) Patched genesis files to automatically detect and process the most recent input file.
  • (Action) Performed “Live Fire” simulation using a dummy rock collector concept to verify end-to-end integrity.
  • (Observation) Validated that new constraints successfully forced correct formatting and logic flow.

Context

  • The system was failing to trigger “Depth Probe” mode because the intermediate agent was writing conversational text instead of the expected checkbox format.
  • Input handling was brittle, requiring manual directory clearing before every run.
  • Cumulative intelligence (Knowledge Base) was active but invisible in final outputs.

Actions

  • Implemented a four-phase fix: (1) Created Golden Examples, (2) Enforced Strict Prompting, (3) Mandated KB Transparency, (4) Reduced Input Friction.
  • Standardized terminology across all prompts to avoid file naming confusion.
  • Executed a full dry-run audit followed by a live simulation to confirm the fix.

Observations

  • LLMs require “few-shot” examples (Golden Files) to maintain strict formatting over time; instructions alone are susceptible to drift.
  • “Lazy” agents tend to revert to prose explanations unless explicitly forbidden by negative constraints.
  • The system is now robust enough to handle ambiguous inputs without breaking the automation pipeline.

Open Threads

  • Monitor for future format drift if underlying model behavior changes significantly.

Boundary Reminder: Seeds. No maintenance. No roadmap.