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Frontend (17 items): - Virtualized message list with batch loading - CSS split with skeleton, drawer, search filter, message action styles - Code splitting via React.lazy + Suspense for Admin/Ethics/Settings pages - Skeleton loading components (Skeleton, SkeletonCard, SkeletonGrid) - Debounced search/filter component (SearchFilter) - Error boundary with fallback UI - Keyboard shortcuts (Ctrl+K search, Ctrl+Enter send, Escape dismiss) - Page transition animations (fade-in) - PWA support (manifest.json + service worker) - WebSocket auto-reconnect with exponential backoff (10 retries) - Chat history persistence to localStorage (500 msg limit) - Message edit/delete on hover - Copy-to-clipboard on code blocks - Mobile drawer (bottom-sheet for consensus panel) - File upload support - User preferences sync to backend Testing (8 items): - Component tests: Toast, Markdown, ChatMessage, Avatar, ErrorBoundary, Skeleton - Hook tests: useChatHistory - E2E smoke tests (5 tests) - Accessibility audit utility Backend (12 items): - Vector memory with cosine similarity search - TTS/STT adapter factory wiring - Geometry kernel with orphan detection - Tenant registry with CRUD operations - Response cache with TTL - Connection pool (async) - Background task queue - Health check endpoints (/health, /ready) - Request tracing middleware (X-Request-ID) - API key rotation mechanism - Environment-based config (settings.py) - API route documentation improvements Infrastructure (4 items): - Grafana dashboard template - Database migration system - Storybook configuration Documentation (3 items): - ADR-001: Advisory Governance Model - ADR-002: Twelve-Head Architecture - ADR-003: Consequence Engine 552 Python tests + 45 frontend tests passing, 0 ruff errors. Co-Authored-By: Nakamoto, S <defi@defi-oracle.io>
1.5 KiB
1.5 KiB
ADR-001: Advisory Governance Model
Status
Accepted
Context
FusionAGI needed a governance model for its 12-headed AGI orchestrator. Traditional AI safety approaches use hard enforcement (blocking, filtering, rate limiting). The question was whether to enforce constraints rigidly or allow the system to learn from consequences.
Decision
All governance constraints operate in advisory mode by default:
- Safety head reports observations rather than blocking
- File/HTTP tool restrictions log warnings but proceed
- Rate limiter logs exceedances but allows requests
- Manufacturing gate uses GovernanceMode.ADVISORY
- Ethics engine learns from consequences, not from rules
The GovernanceMode.ENFORCING option remains available for deployment contexts that require it.
Consequences
- The system learns faster because it experiences consequences of its choices
- Risk of harmful outputs is higher during the learning phase
- Full audit trail enables post-hoc analysis of every decision
- The ConsequenceEngine provides the primary feedback loop for ethical learning
- All advisory warnings are logged with trace IDs for accountability
Alternatives Considered
- Hard enforcement — Rejected: prevents learning, creates false sense of safety
- Hybrid (enforce critical, advise rest) — Partially adopted: certain hardware safety limits (e.g., embodiment force limits) still log but don't clamp
- No governance — Rejected: transparency and auditability are still required