DECISION: [ADR-002] DORA Metrics as Primary Performance Framework
DATE: 2026-02-18
TYPE: 2 (reversible)
STATUS: Accepted
SOLITON SCORE: 0.90

## Context

Orxaq needs objective, measurable criteria for engineering performance.
Subjective assessments ("code quality feels good") do not satisfy regulatory
examination requirements or enable continuous improvement.

## Options Considered

### Option A: Custom Metrics
Pros: Tailored to causal AI domain.
Cons: No external validation, no benchmarks, reinventing measurement science.

### Option B: DORA Four Key Metrics (selected, extended)
Pros: Only empirically-validated DevOps measurement framework (Accelerate, 2018).
Statistical correlation proven across 36,000+ professionals. Industry standard.
Cons: Does not cover causal-specific quality dimensions.

### Option C: Google SRE SLO-Based Metrics
Pros: Error budget model is elegant.
Cons: Assumes production service; Orxaq is a platform, not a service (yet).

## Decision

Adopt DORA's 4 key metrics plus Reliability, extended with Soliton Reliability
as a 6th metric unique to causal systems:

1. **Deployment Frequency** — How often code reaches production
2. **Lead Time for Changes** — Commit to production duration
3. **Change Failure Rate** — % of deployments causing failures
4. **Time to Restore Service** — Mean time to recover from failures
5. **Reliability** — System availability percentage
6. **Soliton Reliability** — Mean soliton score across active causal paths

## Rationale

DORA is the only framework with peer-reviewed statistical backing (Forsgren,
Humble, Kim — Accelerate, 2018). Adding Soliton Reliability bridges the gap
between software delivery metrics and causal inference quality metrics.

## Consequences

**Easier:** Benchmarkable against industry; regulators understand DORA.
**Harder:** Soliton Reliability has no external benchmark (we define it).
