Pattern Proposal: Compliance-Driven Health Scoring

Summary

Computes composite governance health ratings from agent-extracted compliance findings and traceable operational metrics.

Candidate Pattern

• ID: compliance-driven-health-scoring
• Classification: intelligence
• Maturity: established
• Type: implemented

Observed Signals

No specific signals listed.

Evidence

Supporting Evidence

• src/cas_runner/governance/compliance_engine.py (ComplianceEngine) - Scans workspace artifacts against defined compliance rules.
• src/cas_runner/dashboard/health_model.py (HealthStatus, calculate_health, get_health_reasons) - Computes health score enum and human-readable diagnostic reasons.
• src/cas_runner/governance/models.py (GovernanceHealth, ComplianceFinding, ComplianceMetrics) - Typed models holding evaluation findings and computed metrics.

Scope Assessment

• Evidence Strength: high
• Confidence: high
• Applies to: ideas-to-life-cas

Rationale

• Intent: Transform disparate rule compliance violations into standardized health indicators (e.g., Healthy, Degraded, Critical).
• Context: Evaluated after rule verification steps and exposed to dashboards and workflow gates.
• Force: Individual rule violations vary wildly in severity, making binary pass/fail gates too blunt.
• Force: Engineers and autonomous agents need actionable diagnostic reasons when a health rating drops.

Traceability

Artifact source: proposed-patterns