ADR-022: Semantic Failure Management

Status: Accepted Version: 1.0 Date: 2025-10-01 Supersedes: N/A Related ADRs: N/A Related PRDs: N/A

Context

As SEA-Forge™ introduces automated semantic enforcement (SEA-DSL Policy), inferential reasoning (Knowledge Graph), and AI-generated artifacts, semantic failures become inevitable. These include:

• Contradictory but locally valid business rules.
• Divergence between inferred knowledge and intended meaning.
• Conflicts between compliance, architecture, and business urgency.
• AI-generated artifacts that are semantically correct yet operationally harmful.

Without an explicit model, SEA™ risks silent semantic drift, over-automation of authority, and loss of human agency.

Decision

We will implement an explicit Semantic Failure Management Model consisting of:

1. Semantic Failure Classification

• Logical Contradiction: Policies individually valid but jointly unsatisfiable.
• Inferential Divergence: KG inference contradicts DSL intent.
• Projection Mismatch: Isomorphic projections diverge across layers.
• Priority Conflict: Business urgency vs. Compliance constraints.
• Cognitive Hazard: Correct but misleading artifacts.

2. Semantic Debt Tracking

• A Semantic Debt Ledger will track unresolved failures as first-class entities.
• “Semantic Debt” corresponds to technical debt but applies to meaning, intent, and interpretation.

3. Authority & Escalation Boundaries

• SEA™ SHALL NOT autonomously resolve:
  • Contradictory business meanings.
  • Regulatory interpretations.
  • Ethical trade-offs.
• Human authority is required for these decisions.
• See SDS-031: Authority & Ownership Boundaries for:
  • Complete RBAC role definitions (Domain Steward, Architecture Governor, Security Officer, etc.)
  • RACI matrix for semantic governance decisions
  • Non-delegable decision catalog
  • Escalation protocols and emergency procedures

4. Failure-Aware System Behavior

• Enforcement degradation (Block -> Warn -> Advisor).
• Artifact labeling (“Provisional”, “Contested”).

Rationale

Semantic failures are fundamentally different from runtime errors:

• They concern meaning, not mechanics
• They often have no “correct” resolution, only trade-offs
• They require human judgment for resolution
• They accumulate as debt if left unaddressed

An explicit management model prevents silent drift, preserves human agency, and makes power judgments visible.

Constraints

• MUST classify all semantic failures using the defined taxonomy
• MUST track unresolved failures in a Semantic Debt Ledger
• MUST NOT autonomously resolve contradictory business meanings, regulatory interpretations, or ethical trade-offs
• MUST require human authority for non-delegable decisions
• MUST support enforcement degradation (Block -> Warn -> Advisor)
• MUST label artifacts with provisional/contested status when applicable

Quality Attributes

• Prevents silent semantic drift
• Preserves human agency in critical decisions
• Makes power judgments explicit
• Tracks semantic debt as first-class entities
• Supports graduated enforcement

Bounded Contexts Impacted

• Semantic Core
• Knowledge Layer
• Governance Layer
• AI Agent Runtime
• Artifact Pipeline

Consequences

Positive

• Prevents silent drift
• Preserves human agency
• Makes power judgments explicit
• Semantic debt becomes visible and manageable

Negative

• Adds latency to decision-making
• Requires “Semantic Owner” roles
• Additional complexity in failure classification

Additional Notes

Related Specifications

• SDS-031: Authority & Ownership Boundaries