Full optimization: 38 improvements across frontend, backend, infrastructure, and docs

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>

docs/adr/001-advisory-governance.md

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+# 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
+1. **Hard enforcement** — Rejected: prevents learning, creates false sense of safety
+2. **Hybrid (enforce critical, advise rest)** — Partially adopted: certain hardware safety limits (e.g., embodiment force limits) still log but don't clamp
+3. **No governance** — Rejected: transparency and auditability are still required

docs/adr/002-twelve-head-architecture.md

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+# ADR-002: Twelve-Head (Dvādaśa) Architecture
+
+## Status
+Accepted
+
+## Context
+Multi-agent systems typically use 2-5 agents with fixed roles. FusionAGI needed a system that could analyze problems from many perspectives simultaneously while maintaining coherent output.
+
+## Decision
+The orchestrator decomposes every query across **12 specialized heads**:
+
+| Head | Role |
+|------|------|
+| Logic | Logical reasoning and consistency |
+| Research | Source evaluation and synthesis |
+| Systems | Architecture and integration |
+| Strategy | Long-term planning |
+| Product | User experience and design |
+| Security | Threat analysis |
+| Safety | Risk observation (advisory) |
+| Reliability | Fault tolerance |
+| Cost | Resource optimization |
+| Data | Statistical reasoning |
+| DevEx | Developer experience |
+| Witness | Audit and observation |
+
+The Witness head is special: it observes but doesn't contribute to the consensus.
+
+## Consequences
+- Comprehensive analysis from 12 angles on every query
+- Higher latency (12 parallel LLM calls) but better quality
+- The InsightBus enables cross-head learning
+- Each head has a unique color identity in the UI for visual distinction
+- The consensus mechanism must handle disagreement gracefully
+
+## Alternatives Considered
+1. **3-5 heads** — Rejected: insufficient perspective diversity
+2. **Dynamic head count** — Future consideration: some queries don't need all 12
+3. **Hierarchical heads** — Rejected: flat structure promotes equal consideration

docs/adr/003-consequence-engine.md

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+# ADR-003: Consequence Engine for Ethical Learning
+
+## Status
+Accepted
+
+## Context
+Traditional AI ethics systems use static rules (constitutional AI, RLHF reward models). FusionAGI needed a system that could learn ethical behavior from experience — understanding that every choice carries consequences and that risk/reward assessment improves with data.
+
+## Decision
+Implemented a **ConsequenceEngine** that:
+1. Records every choice the system makes (action + alternatives considered)
+2. Estimates risk and reward before acting
+3. Records actual outcomes after execution
+4. Computes "surprise factor" (prediction error)
+5. Feeds into AdaptiveEthics for lesson generation
+6. Uses adaptive risk memory window that grows with experience
+
+The weight system for ethical lessons is **unclamped** — extreme outcomes can push lesson weights below 0 (strong negative signal) or above 1.
+
+## Consequences
+- The system develops genuine experiential ethics rather than rule-following
+- Early-stage behavior may be more exploratory (higher risk)
+- All consequence records are persisted via PersistentLearningStore
+- Cross-head learning via InsightBus amplifies ethical insights
+- The SelfModel's values evolve based on consequence feedback
+
+## Alternatives Considered
+1. **RLHF-style reward model** — Rejected: requires human feedback loop, doesn't scale
+2. **Constitutional AI** — Rejected: static rules, doesn't learn
+3. **No ethics system** — Rejected: need accountability and learning signal