DECISION: [ADR-003] Machine-Readable ADRs as Decision Substrate
DATE: 2026-02-18
TYPE: 1 (irreversible)
STATUS: Accepted
SOLITON SCORE: 0.80

## Context

Standard ADRs (Nygard, 2011) capture decisions in human-readable markdown.
Orxaq's World DAG needs to ingest architectural decisions as causal nodes —
decisions cause constraints, which cause implementations, which cause behaviors.

## Options Considered

### Option A: Standard ADR Format
Pros: Simple, widely understood, low friction.
Cons: No machine-readable fields, no DAG integration, no scoring.

### Option B: YAML-Based Decision Records
Pros: Machine-parseable, schema-validatable.
Cons: Less human-readable, poor for narrative context.

### Option C: Extended ADR with Structured Fields (selected)
Pros: Human-readable narrative + machine-parseable header fields (Type,
Soliton Score, Status, Date). Can be parsed by DAG population scripts.
Cons: Slightly more ceremony per ADR.

## Decision

Extend the standard ADR format with:
- **TYPE**: 1 (irreversible) or 2 (reversible), from Amazon's decision taxonomy
- **SOLITON SCORE**: [0.0-1.0] confidence in decision resilience
- **STATUS**: Lifecycle tracking (Proposed → Accepted → Deprecated → Superseded)
- **DATE**: ISO 8601 for temporal ordering

These fields serve both human review (Operator examination) and machine
consumption (World DAG population).

## Rationale

Decisions are causal. ADR-003 ← ADR-000 ← {Analysis Framework} is a causal
chain. Making this chain machine-readable allows the World DAG to model its
own governance structure — a self-referential property required by ADR-000 Part V.

## Consequences

**Easier:** DAG can be populated from ADRs; traceability is automatic.
**Harder:** Every ADR must include structured fields (minor overhead).
