feat: Add domain architecture and extract core services from Prism Console

## Domain Architecture
- Complete domain-to-service mapping for 16 verified domains
- Subdomain architecture for blackroad.systems and blackroad.io
- GitHub organization mapping (BlackRoad-OS repos)
- Railway service-to-domain configuration
- DNS configuration templates for Cloudflare

## Extracted Services

### AIops Service (services/aiops/)
- Canary analysis for deployment validation
- Config drift detection
- Event correlation engine
- Auto-remediation with runbook mapping
- SLO budget management

### Analytics Service (services/analytics/)
- Rule-based anomaly detection with safe expression evaluation
- Cohort analysis with multi-metric aggregation
- Decision engine with credit budget constraints
- Narrative report generation

### Codex Governance (services/codex/)
- 82+ governance principles (entries)
- Codex Pantheon with 48+ agent archetypes
- Manifesto defining ethical framework

## Integration Points
- AIops → infra.blackroad.systems (blackroad-os-infra)
- Analytics → core.blackroad.systems (blackroad-os-core)
- Codex → operator.blackroad.systems (blackroad-os-operator)

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>

services/codex/entries/022-adversarial-training-cvar.md

@@ -0,0 +1,27 @@
+# Codex 22 — Adversarial Training with CVaR — Robust by the Tail
+
+**Fingerprint:** `23064887b1469b19fa562e8afdee5e9046bedf99aa9cd7142c35e38f91e6fef2`
+
+## Aim
+Optimize models for worst-case performance by focusing on tail risk instead of average loss.
+
+## Core
+- Minimize conditional value at risk (CVaR) at level \(\alpha\): \(\min_\theta \ \text{CVaR}_\alpha(\ell(f_\theta(x + \delta), y))\) subject to \(\|\delta\| \le \epsilon\).
+- Generate hard examples through projected gradient descent (PGD) or expectation over transformation (EOT) loops.
+- Use randomized smoothing or Lipschitz bounds to certify robustness where feasible.
+
+## Runbook
+1. In the inner loop, create adversarial perturbations, tracking tail losses throughout training.
+2. In the outer loop, optimize parameters to minimize CVaR while enforcing a floor on clean accuracy.
+3. Certify robustness post-training and log certified radii alongside accuracy metrics.
+
+## Telemetry
+- Tail loss trajectories and CVaR estimates.
+- Gap between clean and robust accuracy.
+- Certified radii coverage across validation sets.
+
+## Failsafes
+- When tail loss exceeds budget, reduce exposure, increase regularization, or pause deployment.
+- Require human review before shipping models whose robustness certificates regress.
+
+**Tagline:** Train for the attacks you dread, not the averages you like.