System Health Check

Overview

Evaluate if the GOSM system needs improvement

Steps

Step 1: Gather system telemetry

Collect all available system data for analysis:

1. Execution metrics:

   • Goals processed (attempted, completed, failed)
   • Strategies selected and their outcomes
   • Procedures executed and their success rates
   • Average time to goal completion

2. Quality indicators:

   • User satisfaction signals (if available)
   • Rework rate (how often plans/approaches changed)
   • Error rate by type

3. Coverage data:

   • Domains active vs inactive
   • Procedure library utilization
   • Gate coverage and activation

4. Trend data:

   • Metrics over time (improving? degrading?)
   • Anomalies or sudden changes

Step 2: Evaluate procedure health

Analyze the health of the procedure library:

1. Success rate analysis:

   • Which procedures succeed most/least?
   • Are failure rates increasing for any procedures?
   • Are there patterns in failures?

2. Coverage analysis:

   • Which domains have good procedure coverage?
   • What operations lack procedures?
   • Are there redundant procedures?

3. Quality analysis:

   • Do procedures produce quality outputs?
   • Are procedures well-documented?
   • Do procedures have proper verification?

4. Usage analysis:

   • Which procedures are most/least used?
   • Are there procedures that should be retired?
   • Are there procedures that need updating?

Step 3: Evaluate gate health

Analyze the health of gate evaluations:

1. Calibration analysis:

   • Are gates passing when they should?
   • Are gates failing when they should?
   • False positive rate (passes that shouldn’t)
   • False negative rate (fails that shouldn’t)

2. Coverage analysis:

   • Are all critical decision points gated?
   • Are there missing gates?
   • Are there redundant gates?

3. Accuracy analysis:

   • Do gate outcomes correlate with actual success?
   • Are gate criteria well-defined?
   • Are gate thresholds appropriate?

4. Performance analysis:

   • Are gates adding value or just overhead?
   • Time spent on gate evaluations
   • Gates that consistently rubber-stamp

Step 4: Evaluate execution efficiency

Analyze how efficiently goals are being achieved:

1. Path efficiency:

   • Average steps to goal completion
   • Unnecessary detours or loops
   • Optimal vs actual path comparison

2. Time efficiency:

   • Time to first meaningful output
   • Time to goal completion
   • Bottleneck identification

3. Resource efficiency:

   • Compute/effort per goal
   • Rework and waste
   • Parallelization opportunities

4. Decision quality:

   • Strategy selection accuracy
   • Procedure selection accuracy
   • Course correction frequency

Step 5: Evaluate learning and adaptation

Analyze if the system is improving over time:

1. Trend analysis:

   • Is success rate improving?
   • Is efficiency improving?
   • Is quality improving?

2. Learning indicators:

   • Are new procedures being added effectively?
   • Are procedures being refined based on feedback?
   • Is the system adapting to user patterns?

3. Knowledge quality:

   • Is stored knowledge accurate?
   • Is information becoming stale?
   • Are knowledge gaps being filled?

4. Meta-learning:

   • Is the system better at self-assessment?
   • Are health checks leading to improvements?
   • Is the improvement process itself improving?

Step 6: Assess self-referential integrity

Evaluate if the system can reliably improve itself:

1. Meta-procedure health:

   • Are meta-procedures (like this one) working?
   • Can the system discover new procedures?
   • Can the system refine goals effectively?

2. Self-assessment accuracy:

   • Do health checks identify real problems?
   • Are recommendations actionable?
   • Are improvements actually implemented?

3. Stability analysis:

   • Could self-modification cause instability?
   • Are there safeguards against degradation?
   • Is there a recovery path if improvements fail?

4. Completeness check:

   • Can the system assess all its components?
   • Are there blind spots in self-awareness?
   • What can’t the system evaluate about itself?

Step 7: Calculate overall health

Synthesize dimension scores into overall assessment:

1. Calculate weighted health score:

   • Procedure health: 25%
   • Gate health: 15%
   • Execution efficiency: 25%
   • Learning & adaptation: 20%
   • Self-referential integrity: 15%

2. Determine health status:

   • healthy: score >= 0.8, no critical issues
   • needs_attention: 0.6 <= score < 0.8, or moderate issues
   • critical: score < 0.6, or any critical issues

3. Identify strengths:

   • Dimensions scoring > 0.8
   • Areas showing improvement trend
   • Things working as designed

4. Identify concerns:

   • Dimensions scoring < 0.7
   • Areas showing declining trend
   • Patterns in failures

Step 8: Generate improvement plan

Create actionable improvement recommendations:

1. Immediate actions (do now):

   • Critical issues that need immediate attention
   • Quick wins with high impact
   • Safety or stability concerns

2. Short-term improvements (this week/month):

   • High-priority procedure gaps
   • Calibration adjustments
   • Efficiency optimizations

3. Long-term roadmap (this quarter):

   • New capabilities to add
   • Major refactoring needed
   • Learning system improvements

4. New procedures needed:

   • Gaps identified during analysis
   • Priority and complexity
   • Dependencies

When to Use

• Scheduled periodic health assessment (weekly/monthly)
• After a sequence of execution failures
• When system performance seems degraded
• Before major system updates or changes
• When adding new domains or capabilities
• After significant usage period to calibrate
• When procedures consistently fail or produce poor results
• When user satisfaction or trust appears to decline
• After recovering from a critical failure

Verification

• All health dimensions have been evaluated with scores
• Data gaps are acknowledged and don’t invalidate conclusions
• Issues are specific enough to act on
• Recommendations are prioritized by impact and urgency
• Health status matches the evidence
• Self-referential limitations are acknowledged