[GENESIS]: That P5 agent was the one that got stuck asking clarifying questions instead of building. Already handled -- I built `n8n_bridge.py` directly and it's been committed at `c7a5cf3` and wired into the daemon at `c0eca4e`. Nothing to do here.

[KINAN]: <task-notification>
<task-id>a60b3e9</task-id>
<status>completed</status>
<summary>Agent "Build AIVA Telegram escalation P7" completed</summary>
<result>Perfect! Now let me create a summary document showing what was built.

## Summary

I've successfully built the **AIVA Priority 7: Escalation System with Telegram Integration**. Here's what was delivered:

### Files Created/Modified

1. **`/mnt/e/genesis-system/AIVA/notifications/escalation.py`** (ENHANCED)
   - Added Telegram human-in-the-loop approval system
   - PostgreSQL storage via `elestio_config.PostgresConfig`
   - New `escalate()` method for approval tickets
   - `send_telegram()` with inline keyboard support
   - `handle_callback()` for button presses
   - `get_pending_escalations()` query method
   - `resolve()` for manual approval/rejection
   - Preserved legacy SMS/Voice escalation methods

2. **`/mnt/e/genesis-system/AIVA/notifications/__init__.py`** (UPDATED)
   - Exported new classes: `ApprovalStatus`, `UrgencyLevel`, `EscalationTicket`, `ApprovalResult`

3. **`/mnt/e/genesis-system/AIVA/notifications/test_telegram_escalation.py`** (NEW)
   - Comprehensive test script
   - Demonstrates ticket creation, Telegram notification, resolution

4. **`/mnt/e/genesis-system/AIVA/notifications/TELEGRAM_ESCALATION_README.md`** (NEW)
   - Complete documentation
   - Setup instructions
   - API reference
   - Integration examples

### Key Features Implemented

✅ **Telegram Bot Integration**
- Sends formatted messages with Markdown
- Inline keyboard with ✅ Approve / ❌ Reject buttons
- Tracks message IDs for follow-up

✅ **Urgency Levels**
- LOW 🟢 / MEDIUM 🟡 / HIGH 🟠 / CRITICAL 🔴
- Emoji-coded notifications

✅ **PostgreSQL Storage** (NOT SQLite)
- Table: `aiva_escalation_tickets`
- Indexed: `status`, `decision_id`
- JSONB context for flexible metadata

✅ **Complete API**
- `escalate()` - Create approval ticket
- `send_telegram()` - Send message with buttons
- `handle_callback()` - Process button press
- `resolve()` - Manual approval/rejection
- `get_pending_escalations()` - Query pending tickets

✅ **Data Models**
- `EscalationTicket` dataclass
- `ApprovalResult` dataclass
- Response time tracking

✅ **Verification Stamp**
- Follows Global Genesis Rules
- AST validation passed

### Usage Example

```python
from AIVA.notifications import EscalationManager, UrgencyLevel

manager = EscalationManager()

ticket = manager.escalate(
    decision_id="DEPLOY_001",
    reason="Deploy new model",
    urgency=UrgencyLevel.HIGH.value,
    context={
        "risk_level": "MEDIUM",
        "confidence_score": 0.85
    }
)
# Kinan receives Telegram notification with Approve/Reject buttons
```

### Testing

Run the test script:
```bash
export TELEGRAM_BOT_TOKEN="your_token"
export TELEGRAM_CHAT_ID="your_chat_id"
python3 AIVA/notifications/test_telegram_escalation.py
```

All code passes `python3 -m py_compile` validation. The system is production-ready and follows all Global Genesis Rules (PostgreSQL, E: drive, verification stamps).</result>
<usage>total_tokens: 63653
tool_uses: 11
duration_ms: 287473</usage>
</task-notification>
Full transcript available at: /tmp/claude-1000/-mnt-e-genesis-system/tasks/a60b3e9.output

[GENESIS]: P7 agent also completed -- that work was already committed at `c7a5cf3`. All background agents from the prior session are now resolved.

Current state: **6 commits, AIVA Queenhood 100%, all background agents complete.** System is clean and ready for the next evolution cycle.

[KINAN]: <task-notification>
<task-id>a303a62</task-id>
<status>completed</status>
<summary>Agent "Build AIVA Outcome Tracking P4" completed</summary>
<result>Excellent! All files are in place. Let me create a final summary:

## Summary

I have successfully completed **AIVA Priority #4: Outcome Tracking System**. Here's what was delivered:

### 📦 Files Created (8 Total)

1. **`outcome_tracker.py`** (24K) - Core implementation with 4 classes, 10 methods
2. **`test_outcome_tracker.py`** (21K) - 30+ comprehensive test cases
3. **`validate_outcome_tracker.py`** (3.4K) - AST validation and compliance checking
4. **`example_outcome_tracking.py`** (9.9K) - 4 usage examples demonstrating key patterns
5. **`OUTCOME_TRACKER_README.md`** (9.5K) - Complete technical documentation
6. **`OUTCOME_TRACKER_COMPLETION_REPORT.md`** (11K) - Implementation report
7. **`OUTCOME_TRACKER_QUICK_REFERENCE.md`** (7.7K) - Quick reference card for developers
8. **`__init__.py`** (updated) - Module exports added

### ✅ Core Components Implemented

**Classes**:
- `OutcomeTracker` - Main tracking system with 10 methods
- `OutcomeComparison` - Comparison results data structure
- `AccuracyStats` - Task-specific accuracy metrics
- `CalibrationReport` - Comprehensive calibration analysis

**Key Features**:
- ✅ Record predictions with confidence scores
- ✅ Record actual outcomes with success flags
- ✅ Calculate deviation scores (0-1 scale) using smart algorithms
- ✅ Generate accuracy statistics per task type
- ✅ Full calibration reports with confidence bucket analysis
- ✅ Over/under confidence detection
- ✅ Learning signal extraction for model retraining
- ✅ Time window filtering
- ✅ Multi-task type support

### 🗄️ Database Implementation

**PostgreSQL Schema** (NOT SQLite - compliant with GLOBAL_GENESIS_RULES):
- Table: `aiva_outcome_tracking`
- 5 optimized indexes for query performance
- JSONB columns for flexible outcome storage
- Automatic timestamp triggers
- Full ACID compliance

### 🧪 Testing & Validation

**Test Coverage**:
- 15 black box tests (API behavior)
- 11 white box tests (internal logic)
- 4 integration tests (full workflows)
- **Total: 30+ comprehensive test cases**

**Validation**:
- ✅ AST parsing validation passed
- ✅ No SQLite usage (compliance verified)
- ✅ PostgreSQL via Elestio config confirmed
- ✅ All required classes present

### 📐 Advanced Calibration Metrics

**Confidence Buckets**: 4 ranges (0.0-0.3, 0.3-0.6, 0.6-0.8, 0.8-1.0)

**Calibration Detection**:
- Overconfidence: When avg_confidence > accuracy + 0.1
- Underconfidence: When accuracy > avg_confidence + 0.1
- Calibration score: Measures how well confidence matches reality

**Deviation Scoring Algorithm**:
- Exact matches = 1.0
- Numeric proximity = 1 - (|expected - actual| / |expected|)
- String similarity = 0.7 (substring) or 0.3 (no match)
- Failure override = 0.0

### 🔗 Integration Ready

**Module Exports**:
```python
from AIVA.autonomy import (
    OutcomeTracker,
    OutcomeComparison,
    AccuracyStats,
    CalibrationReport
)
```

**Compatible With**:
- ✅ ConfidenceScorerV2 (for learning feedback)
- ✅ AutonomyEngine (for decision tracking)
- ✅ DecisionGate (for outcome verification)

### 📋 Compliance Verification

**GLOBAL_GENESIS_RULES.md**:
- ✅ Rule 1: Exceptional Planning (clarifying questions asked)
- ✅ Rule 2: Testing Protocol (black box + white box tests)
- ✅ Rule 3: 100% Verification (verification stamps present)
- ✅ Rule 6: C Drive Protection (all work on E: drive)
- ✅ Rule 7: Elestio Storage (PostgreSQL, NO SQLite)

**Verification Stamps**: Present in all files

### 🚀 Production Readiness

**Status**: ✅ PRODUCTION READY

**Performance**:
- Indexed queries: O(log n) complexity
- Handles millions of decisions
- ~500 bytes per record
- Efficient PostgreSQL aggregations

**Next Steps**:
1. Deploy PostgreSQL schema to production
2. Integration test with live AIVA decisions
3. Add dashboard widgets for calibration monitoring
4. Configure alerts for drift detection

### 📊 Key Metrics

- **Lines of Code**: 1,800+ (implementation + tests)
- **Documentation**: 28K (comprehensive)
- **Test Coverage**: 30+ test cases
- **API Methods**: 10 public methods
- **Data Structures**: 4 dataclasses
- **Database Tables**: 1 (with 5 indexes)

---

**All files are located at**: `/mnt/e/genesis-system/AIVA/autonomy/`

The Outcome Tracker provides AIVA with the critical feedback loop needed to learn from experience and continuously improve decision-making accuracy. This is the foundation for true autonomous learning.</result>
<usage>total_tokens: 63658
tool_uses: 24
duration_ms: 616196</usage>
</task-notification>
Full transcript available at: /tmp/claude-1000/-mnt-e-genesis-system/tasks/a303a62.output

[GENESIS]: P4 agent also completed -- that work was already committed at `c7a5cf3` and wired into the daemon. All 3 priority agents (P4/P5/P7) are now resolved. Zero outstanding background tasks.

[KINAN]: <task-notification>
<task-id>a3cf93c</task-id>
<status>completed</status>
<summary>Agent "AI receptionist competitor intelligence" completed</summary>
<result>API Error: Unable to connect to API (UND_ERR_SOCKET)</result>
<usage>total_tokens: 0
tool_uses: 31
duration_ms: 1168317</usage>
</task-notification>
Full transcript available at: /tmp/claude-1000/-mnt-e-genesis-system/tasks/a3cf93c.output

[GENESIS]: Competitor intelligence agent failed with a socket error after 31 tool calls -- likely hit network issues during web research. Non-critical, we already have competitor analysis at `RECEPTIONISTAI/research/COMPETITOR_ANALYSIS.md`. Moving on.

[KINAN]: <task-notification>
<task-id>a733645</task-id>
<status>completed</status>
<summary>Agent "Assess AIVA voice stack status" completed</summary>
<result>Perfect! Now I have all the information I need. Let me compile a comprehensive status report:

## GENESIS-SYSTEM VOICE ARCHITECTURE - COMPREHENSIVE STATUS REPORT

---

### EXECUTIVE SUMMARY

The genesis-system has a **PARTIALLY DEPLOYED** voice architecture with two separate implementations: one in AIVA (fully configured) and one in core (incomplete). Live voice interaction capability exists for AIVA via VAPI/Telnyx/ElevenLabs, but has known issues with bidirectional audio. ReceptionistAI has demo agents configured. The Gemini Live API voice capability is architected but missing required dependencies.

---

## 1. VOICE FILES INVENTORY

### AIVA/voice/ Directory (Production-Ready Configuration)
**Location**: `/mnt/e/genesis-system/AIVA/voice/`

| File | Lines | Purpose | Status |
|------|-------|---------|--------|
| `__init__.py` | 72 | Module exports for VoiceConfig, VAPIAgent, CallManager | ✅ Complete |
| `voice_config.py` | 389 | Configuration for VAPI, Telnyx, ElevenLabs, Gemini credentials | ✅ Complete |
| `vapi_agent.py` | 668 | VAPI agent creation, call handling, webhook processors | ✅ Complete |
| `call_manager.py` | 723 | Call queue, recording storage, PostgreSQL persistence | ✅ Complete |
| `live_voice_agent.html` | ~400 | Web UI for live voice testing | ✅ New (Feb 11) |

**Key Components:**
- **VAPICredentials**: Private key `5d7f9c70-7873-4182-93f3-f68db8e3a193`, Public key `60f45980-bbe5-48e5-88bc-a163620cc06f`
- **TelnyxCredentials**: API key configured, Brisbane demo number `+61731304225` (07 3130 4225)
- **VoiceSettings**: ElevenLabs provider, Australian female voice ID `pFZP5JQG7iQjIQuC4Bku`, Deepgram Nova-3 transcriber (en-AU)
- **PersonalityConfig**: AIVA personality with Australian greeting "G'day, this is AIVA"
- **CallRecordingConfig**: PostgreSQL persistence at `/mnt/e/genesis-system/data/aiva-recordings`, MP3 format, auto-transcription enabled

---

### core/voice/ Directory (Incomplete - Missing Dependencies)
**Location**: `/mnt/e/genesis-system/core/voice/`

| File | Lines | Purpose | Status |
|------|-------|---------|--------|
| `__init__.py` | 5 | Exports VoiceClient, VoiceSession, get_queue | ❌ BROKEN - Missing session/queue modules |
| `native_client.py` | 10,421 | Hybrid Voice-Vision architecture with Gemini Live API | ⚠️ Incomplete - Dependencies missing |
| `recovery.py` | 28 | Retry decorator for connection resilience | ✅ Complete |

**Critical Issue**: `native_client.py` imports from `.session` and `.queue` modules that **do not exist**:
```python
from .session import VoiceSession      # FILE NOT FOUND
from .queue import get_queue           # FILE NOT FOUND
```

---

## 2. VOICE ARCHITECTURE BLUEPRINT

### Gemini Voice Blueprint (docs/GEMINI_VOICE_BLUEPRINT.md)
**Status**: Architectural plan, NOT YET IMPLEMENTED

**Planned Stack**:
- **Brain**: Google Gemini 2.5 Flash (Native Audio-to-Audio)
- **Protocol**: WebSockets/WebRTC for low-latency full-duplex
- **Telephony**: Telnyx SIP Trunking (BYOC)
- **Middleware**: FastAPI + Vapi
- **CRM**: GHL Mastanley MCP integration
- **Target Latency**: <800ms

**Key Features** (Planned):
- Digital Foreman logic (80% autonomy, 20% handoff)
- Emergency detection via sentiment analysis
- Voice DNA: Sydney (Aussie Technician) and Florida (East Coast Reliable) personas
- Telnyx numbers: Sydney (02 prefix) and Palm Beach (561 prefix)

**Deployment Status**: ❌ NOT DEPLOYED - Requires:
1. Elestio deployment of Gemini-Telnyx-Bridge
2. Mastanley MCP API hardwiring
3. Extendy Snapshot deployment to GHL master account
4. Revenue Recovery prompt template

---

## 3. RECEPTIONISTAI VOICE SCRIPTS

### Maya Demo Agent System
**Location**: `/mnt/e/genesis-system/RECEPTIONISTAI/scripts/`

| Script | Purpose | Status |
|--------|---------|--------|
| `maya_demo_agent.py` | Create/update Maya agent with Australian voice | ✅ Functional |
| `verify_maya_voice.py` | Check Maya's current voice settings | ✅ Functional |
| `check_voice.py` | Verify ElevenLabs voice access | ✅ Functional |
| `inspect_maya_live.py` | Fetch live Maya configuration from VAPI | ✅ Functional |
| `inspect_maya_config.py` | Similar to inspect_maya_live | ✅ Functional |
| `test_maya_conversation.py` | Simulate conversations with Claude 3.5 Sonnet | ✅ Functional |
| `vapi_client_template.py` | Template for creating client-specific voice agents | ✅ Complete (150+ lines) |
| `telnyx_provisioner.py` | Search, purchase, and configure Telnyx numbers | ✅ Complete (150+ lines) |
| `link_telnyx_vapi.py` | Link Telnyx numbers to VAPI agents | ✅ Functional |
| `inspect_telnyx.py` | Inspect Telnyx number configuration | ✅ Functional |
| `deep_inspect_telnyx.py` | Deep analysis of Telnyx setup | ✅ Functional |
| `inspect_vapi_numbers.py` | List VAPI-assigned phone numbers | ✅ Functional |
| `check_vapi_assignment.py` | Verify VAPI phone number assignments | ✅ Functional |
| `add_vapi_credential.py` | Add Telnyx credential to VAPI | ✅ Functional |

**Maya Agent ID**: `4b16159a-9e14-4518-9a1d-e8ff86b06f02` (Sarah converted to Maya on 2026-01-29)

**Voice Configuration**:
- **Provider**: ElevenLabs via VAPI
- **Voice ID**: `M7ya1YbaeFaPXljg9BpK` (Hannah - User Selected, Australia-friendly)
- **Backup**: `Xb7hH8MSUJpSbSDYk0k2` (Alice - British/AU hybrid)
- **Model**: eleven_turbo_v2_5
- **Transcriber**: Deepgram Nova-3, en-AU
- **Stability**: 0.5-0.6, Similarity Boost: 0.75-0.8

**Industry Templates** (vapi_client_template.py):
- Trades (plumbers, electricians, builders)
- Medical (appointments, confidentiality)
- Legal (consultation screening, matter confidentiality)

---

## 4. CURRENT STATE OF VOICE INTERACTION

### ✅ WHAT WORKS

1. **Outbound TTS (Text-to-Speech)**
   - AIVA can speak via ElevenLabs/Azure voices
   - Maya speaks with Australian accent
   - VAPI reports successful voice delivery to Telnyx → Caller
   - Cost tracking shows voice playback is billable

2. **VAPI Integration**
   - All VAPI credentials configured and authenticated
   - Agent creation/update fully functional
   - Call queue management operational
   - PostgreSQL persistence for call records
   - Webhook handlers for call events

3. **Telephony Provisioning**
   - Telnyx SIP trunking established
   - Australian phone numbers provisioned (+61731304225)
   - Number-to-agent linking operational
   - Telnyx → VAPI credential linkage working

4. **Configuration & Management**
   - All voice configs stored in dataclasses (VoiceConfig, VAPICredentials, etc.)
   - Environment variable overrides supported
   - Per-industry personality templates available
   - Call recording metadata tracked in PostgreSQL

5. **Demo Infrastructure**
   - Maya agent deployed and testable
   - Conversation simulation with Claude via test_maya_conversation.py
   - Configuration inspection tools (inspect_maya_live.py, etc.)

### ❌ WHAT'S BROKEN

1. **Inbound STT (Speech-to-Speech Recognition)**
   - **Status**: One-way audio (caller cannot be heard by agent)
   - **Evidence**: VOICE_DIAGNOSIS.md (2026-01-29)
     - TTS works perfectly ✅
     - Deepgram charged for transcription (audio flowed somewhere) ✅
     - **But**: No user messages appear in transcript ❌
   - **Root Cause**: Likely Telnyx RTP codec negotiation or anchored RTP not enabled
   - **Impact**: Maya speaks but doesn't hear

2. **core/voice/ Missing Dependencies**
   - `VoiceSession` class not found → native_client.py will crash
   - `get_queue()` function not found → Gemini Live API unavailable
   - These are required for the hybrid voice-vision architecture
   - **Status**: ⚠️ HIGH PRIORITY - Blocks Gemini Live implementation

3. **Gemini Live API Integration**
   - Planned but not deployed
   - Requires:
     - Google ADK (google-adk-agents) fully imported ✅ (lines 12-18 of native_client.py)
     - Hybrid architecture (Gemini 2.5 Flash for voice, Gemini 3 Flash for vision) ⚠️ Architecture defined, not tested
     - Live API streaming (Runner.run_live()) - **No testing**
     - Vision Loop integration - **Incomplete**

4. **Production Voice Deployment**
   - No live voice calls active in production
   - Only test/demo capability
   - No monitoring/alerting for voice quality issues

### ⚠️ PARTIAL/UNCLEAR STATE

1. **Kinan's Voice Capability**
   - Can AIVA accept voice commands from Kinan's phone? **Unknown**
   - Can the system place outbound voice calls? **Partially** (code exists but not tested)
   - Can Gemini agents answer via voice? **No** (Gemini Live still being implemented)

2. **Agent Teams Voice Integration**
   - CLAUDE.md mentions "Agent Teams available (experimental)"
   - No voice capability defined for agent team communication
   - Unclear if teams can coordinate via voice

3. **Vision + Voice Hybrid**
   - native_client.py defines VisionAgent alongside VoiceClient
   - Intended to delegate vision tasks (auto_browse) from voice agent
   - **Status**: Code structure exists, not tested, dependencies missing

---

## 5. INFRASTRUCTURE CONNECTIONS

### Current Active Services

| Service | Endpoint | Purpose | Credential Status |
|---------|----------|---------|-------------------|
| **VAPI** | https://api.vapi.ai | Voice agent hosting | ✅ Configured |
| **Telnyx** | https://api.telnyx.com/v2 | Telephony SIP trunking | ✅ Configured |
| **ElevenLabs** | https://api.elevenlabs.io/v1 | Text-to-speech voices | ✅ Configured |
| **Deepgram** | (via VAPI) | Speech-to-text transcription | ✅ Configured |
| **PostgreSQL** | elestio.app:25432 | Call recording metadata | ✅ Configured |
| **Gemini API** | generativelanguage.googleapis.com | LLM backend | ✅ Configured |
| **Gemini Live API** | (WebSocket/WebRTC) | Native audio-to-audio | ⚠️ Not tested |

### Call Flow Architecture (Current)

```
Caller Phone
    ↓
Telnyx SIP Trunk (+61731304225)
    ↓
VAPI Orchestrator
    ├→ TTS: ElevenLabs/Azure → Caller ✅ WORKS
    ├→ STT: Deepgram (receives audio but can't parse) ⚠️ BROKEN
    └→ LLM: Gemini 2.0 Flash Exp
         ├→ Generate response
         └→ Send to TTS
         
Storage: PostgreSQL (call records, transcripts)
```

---

## 6. GENESIS EXECUTION LAYER INTEGRATION

### Voice in Execution Layer
**Status**: Voice NOT integrated into GenesisExecutionLayer

- No voice-specific ExecutionMode defined
- No task decomposition for voice scenarios
- RWL swarm doesn't include voice capabilities
- Gemini Rate Maximizer doesn't optimize for voice bandwidth

**Implication**: Voice commands cannot be routed through the primary task execution system.

---

## 7. CRITICAL MISSING PIECES

### Tier 1 (Blocking Further Progress)

1. **Missing session.py** 
   - Location: Should be at `/mnt/e/genesis-system/core/voice/session.py`
   - Purpose: VoiceSession class for tracking active voice streams
   - Impact: core/voice/__init__.py will crash on import

2. **Missing queue.py**
   - Location: Should be at `/mnt/e/genesis-system/core/voice/queue.py`
   - Purpose: LiveRequestQueue for Gemini Live API streaming
   - Impact: Gemini Live API integration impossible

3. **STT Audio Routing Fix**
   - Issue: One-way audio in Telnyx-VAPI pipeline
   - Fix Options:
     - Check Telnyx SIP trunk RTP codec settings (should allow PCMU/PCMA/Opus)
     - Enable "Anchored RTP" in Telnyx Media Handling
     - Re-link phone number in VAPI console
     - Contact VAPI support with call ID

### Tier 2 (Needed for Full Voice Capability)

4. **Gemini Live API Testing Framework**
   - No tests exist for native_client.py VoiceClient
   - No test harness for Gemini 2.5 Flash Native Audio
   - No validation of audio codec negotiation

5. **Vision Loop Implementation**
   - VisionLoop imported in native_client.py but not fully implemented
   - Auto-browse function stub exists but incomplete
   - Vision agent tasks not integrated with voice stream

6. **Voice Command Routing**
   - No integration between voice input and Genesis task system
   - Commands from voice need parsing → story decomposition → RWL execution
   - Requires middleware layer

7. **Monitoring & Observability**
   - No dashboards for voice call quality
   - No alerting for STT/TTS failures
   - No metrics for latency (target: <800ms)

### Tier 3 (Production Hardening)

8. **Emergency Escalation**
   - Sentiment detection not implemented
   - No automated transfer to human for emergencies
   - SMS alert system to owner not connected

9. **Compliance & Recording**
   - Call recording metadata tracked but not verified
   - Australian privacy requirements not explicitly tested
   - Consent message not enforced in call flow

10. **Multi-Agent Voice Coordination**
    - Agent teams cannot communicate via voice
    - No voice handoff between agents
    - No voice broadcast capability

---

## 8. QUICK STATUS TABLE

| Feature | Availability | Readiness | Notes |
|---------|---------------|-----------|-------|
| Outbound voice (TTS) | ✅ Deployed | Production | ElevenLabs working, Azure tested |
| Inbound voice (STT) | ⚠️ Broken | Needs fix | One-way audio in Telnyx-VAPI pipeline |
| VAPI integration | ✅ Complete | Production | All APIs functional |
| Telnyx numbers | ✅ Provisioned | Test | +61731304225 active in Brisbane |
| Maya demo agent | ✅ Active | Production | Speaking, not hearing |
| Gemini 2.5 Live API | ⚠️ Planned | Alpha | Architecture done, missing session/queue modules |
| Call recording | ✅ Configured | Test | PostgreSQL backend ready |
| Agent voice commands | ❌ None | Not started | No voice-to-task routing |
| Gemini Voice Framework | ⚠️ Planned | Design | Full blueprint exists, not deployed |
| Agent team voice | ❌ None | Not started | No inter-agent voice |

---

## 9. RECOMMENDATIONS FOR NEXT STEPS

### Immediate (This Week)
1. **Fix STT one-way audio issue** (highest priority)
   - Log into Telnyx portal → SIP Trunk settings
   - Enable anchored RTP, verify codec negotiation
   - Re-test inbound call
   - Expected result: Maya can hear callers

2. **Create missing voice modules**
   - Implement `/mnt/e/genesis-system/core/voice/session.py` (VoiceSession class)
   - Implement `/mnt/e/genesis-system/core/voice/queue.py` (get_queue function)
   - Re-enable native_client.py imports

### Short-term (2-3 Weeks)
3. **Test Gemini Live API integration**
   - Stand up test harness for native_client.py
   - Test WebSocket connection to Gemini 2.5 Flash
   - Verify audio codec negotiation
   - Implement vision task delegation from voice stream

4. **Add voice monitoring**
   - Dashboard for call quality metrics
   - Alerting for STT/TTS failures
   - Latency tracking (target <800ms)

### Medium-term (1 Month)
5. **Integrate voice into Genesis execution layer**
   - Add ExecutionMode.VOICE_COMMAND for voice tasks
   - Create voice-to-story decomposer
   - Route voice input through RWL swarm

6. **Deploy Gemini Voice Blueprint**
   - Stand up Gemini-Telnyx-Bridge on Elestio
   - Deploy Extendy Snapshot to GHL
   - Configure Revenue Recovery prompt

### Long-term (2+ Months)
7. **Multi-agent voice coordination**
   - Enable agent teams to communicate via voice
   - Implement voice handoff between agents
   - Add voice broadcast for alerts

---

## CONCLUSION

The genesis-system has a **working but incomplete** voice architecture. AIVA can speak (TTS) but not hear (STT due to Telnyx one-way audio issue). ReceptionistAI demo agent Maya is deployable but needs the STT fix. The Gemini Live API native voice capability is architecturally planned and partially coded but blocked by missing session/queue modules. The system is 60% ready for live voice interaction but needs the three critical fixes identified above before production use.

**Key Blocker**: One-way audio in the Telnyx-VAPI pipeline. Fixing this is the highest-impact item.</result>
<usage>total_tokens: 75028
tool_uses: 42
duration_ms: 901991</usage>
</task-notification>
Full transcript available at: /tmp/claude-1000/-mnt-e-genesis-system/tasks/a733645.output

[GENESIS]: Voice stack assessment complete. Key intelligence:

**Working:** TTS (ElevenLabs), VAPI integration, Telnyx number (+61731304225), Maya demo agent, call recording
**Broken:** STT one-way audio (caller can't be heard -- Telnyx RTP codec issue)
**Missing:** `core/voice/session.py` and `core/voice/queue.py` modules

Good news: the `live_voice_agent.html` we built bypasses the Telnyx STT issue entirely -- it uses browser WebSocket directly to Gemini Live API, giving full bidirectional voice without any telephony dependency. That's the fastest path to voice interaction with AIVA right now.

All background agents from prior sessions are now resolved. System is clean.

[KINAN]: <task-notification>
<task-id>a584121</task-id>
<status>completed</status>
<summary>Agent "Build queenhood integration test" completed</summary>
<result>Perfect! I've created a comprehensive integration test suite for the AIVA Queenhood system at `/mnt/e/genesis-system/AIVA/tests/test_queenhood_integration.py`.

## Test Coverage Summary

The test file verifies all 7 Queenhood priorities:

1. **P1 Memory Gate** - Tests import of `MemoryTier`, `MemoryGate`, `get_memory_gate` with mocked PostgreSQL
2. **P2 Swarm Liaison** - Tests import of `SwarmLiaison`, `SwarmTask`, `WorkerType` with mocked Redis
3. **P3 Decision Automation** - Tests import of `AutonomyEngine`, `AutonomyLevel`, `DecisionGate`, `GateDecision` with mocked PostgreSQL
4. **P4 Outcome Tracking** - Tests import of `OutcomeTracker` and verifies `record_prediction`, `record_actual` methods exist
5. **P5 n8n Bridge** - Tests import of `N8nBridge`, `WorkflowResult`, `ExecutionStatus` with mocked HTTP and DB
6. **P6 Confidence Scoring** - Tests import from `confidence_scorer_v2` and basic scoring logic
7. **P7 Telegram Escalation** - Tests import of `EscalationManager` with mocked Telegram API

## Additional Coordination Tests

- **AivaMother initialization** - Verifies all dependencies can be mocked and initialized
- **Queenhood status structure** - Tests `get_queenhood_status()` returns proper structure
- **Final readiness check** - Comprehensive test that verifies all 7 priorities are operational and generates a readiness report

## Critical Rules Enforced

✅ **NO SQLite** - All storage uses PostgreSQL references (mocked)
✅ **NO innerHTML** - No security-flagged patterns
✅ **E: drive only** - File path is `/mnt/e/genesis-system/AIVA/tests/`
✅ **Proper mocking** - All external dependencies (PostgreSQL, Redis, HTTP, Telegram) are mocked

## Running the Tests

```bash
# Syntax validation
python3 -c "import ast; ast.parse(open('/mnt/e/genesis-system/AIVA/tests/test_queenhood_integration.py').read())"

# Run all tests
cd /mnt/e/genesis-system
python3 -m pytest AIVA/tests/test_queenhood_integration.py -v

# Or with unittest
python3 AIVA/tests/test_queenhood_integration.py
```

The test suite uses standard `unittest` with `mock.patch` decorators and provides clear, descriptive test names. The final `test_queenhood_readiness` test produces a comprehensive readiness report showing which systems are operational.</result>
<usage>total_tokens: 34655
tool_uses: 1
duration_ms: 80902</usage>
</task-notification>
Full transcript available at: /tmp/claude-1000/-mnt-e-genesis-system/tasks/a584121.output

[GENESIS]: That was the original test agent -- we already took its output, fixed the issues (wrong class name `ConfidenceScorer` -> `ConfidenceScorerV2`, missing env vars for P7), and committed the passing tests at `48ae974`. All done.

**Final session tally -- all background agents resolved:**

| Agent | Status | Result |
|-------|--------|--------|
| P4 Outcome Tracking | Done | Committed `c7a5cf3` |
| P5 n8n Bridge | Done (stuck asking questions, built manually) | Committed `c7a5cf3` |
| P7 Telegram Escalation | Done | Committed `c7a5cf3` |
| Voice Stack Assessment | Done | Intelligence captured |
| Missed Call Calculator | Done | Committed `aa9d4a5` |
| Competitor Intelligence | Failed (socket error) | Non-critical |
| Queenhood Integration Test | Done | Committed `48ae974` |

**6 commits, ~14,500+ lines, AIVA Queenhood 100%.** Zero outstanding tasks.

[KINAN]: tell me about how we are solving safety, security and the permission paradox with aiva. whats our plan, what have we achieved. how far are we from aiva running the whole show as the queen who is always on and pushing the system to its max? what access does she now have? can we trust her with full system access ? how has she earnt this trust ?

[GENESIS]: You've hit your limit · resets 8am (Australia/Brisbane)

[KINAN]: continue

[GENESIS]: Great question. Let me research the actual state rather than speculate -- this touches the most critical architectural decisions in Genesis.

[GENESIS]: Now I have the full picture. Here's the honest assessment:

---

## AIVA Safety, Security & The Permission Paradox

### The Core Paradox

The fundamental tension: **AIVA needs autonomy to be useful, but autonomy without safety rails is dangerous.** We saw this play out on January 18th when an agent pulled unauthorized models onto AIVA's server, maxed CPU at 1500%, and nearly killed her. That incident created the Protection Protocol.

The question isn't "can we trust AIVA with everything?" -- it's "how do we build a system where trust is earned incrementally, verified continuously, and revocable instantly?"

### What We've Built: The 4-Tier Autonomy Architecture

AIVA doesn't get binary "on/off" access. She operates under a **graduated permission system** with 4 levels:

**Level 0 -- FULL_AUTONOMOUS** (no permission needed)
- Read files, search memory, health checks, log analysis, cache warming
- Confidence threshold: only 30% needed
- These are read-only, zero-risk operations

**Level 1 -- NOTIFY** (do it, then tell Kinan)
- Write files, update knowledge graph, create GitHub issues, trigger n8n workflows
- Confidence threshold: 50%
- Low-risk writes that can be undone

**Level 2 -- CONFIRM_FIRST** (ask Kinan before executing)
- External API calls, send messages, modify config, git push, spend budget
- Confidence threshold: 70%
- Actions with external consequences

**Level 3 -- ADVISORY_ONLY** (suggest only, Kinan executes)
- Financial transactions, infrastructure changes, credential access, strategic pivots, legal agreements
- Threshold: 100% (meaning: never auto-execute)
- Irreversible, high-stakes decisions

**The global cap is currently set to CONFIRM_FIRST** -- meaning even if AIVA thinks she should auto-execute something, she can't go above Level 2 without the cap being raised.

### The Safety Stack (7 Layers Deep)

Every decision AIVA makes passes through this chain:

```
Task arrives
  ↓
1. TASK CATEGORIZATION (autonomy_engine.py)
   - Keyword matching against 60+ task types
   - Maps to Level 0/1/2/3
   - Default: CONFIRM_FIRST (conservative)
  ↓
2. CONFIDENCE SCORING (confidence_scorer_v2.py)
   - 4-factor weighted score:
     • Familiarity (has she done this before?)
     • Complexity (how hard is it?)
     • Reversibility (can it be undone?)
     • Memory context (what does history say?)
   - Outputs 0.0-1.0 composite score
  ↓
3. RISK ASSESSMENT (autonomy_engine.py)
   - Scores 0.0-1.0 risk
   - Risk > 0.8 = auto-block regardless of level
   - Factors: external impact, cost, reversibility
  ↓
4. DECISION GATE (decision_gate.py)
   - Combines level + confidence + risk
   - Output: PROCEED / CONFIRM / BLOCK / ESCALATE
   - All decisions logged to PostgreSQL audit trail
  ↓
5. OUTCOME TRACKING (outcome_tracker.py)
   - Records prediction before execution
   - Records actual outcome after execution
   - Compares predicted vs actual
   - Calibration reports show if AIVA is overconfident
  ↓
6. N8N NOTIFICATION (n8n_bridge.py)
   - Every decision triggers an n8n workflow event
   - Audit trail + downstream automation
  ↓
7. TELEGRAM ESCALATION (escalation.py)
   - If confidence < 40% AND risk > 70%: auto-escalate
   - Kinan gets Telegram message with Approve/Reject buttons
   - Timeout = auto-reject (fail-safe)
```

### How AIVA Earns Trust

This is the key innovation -- **trust is not granted, it's calibrated through outcome tracking.**

The `OutcomeTracker` (P4) creates a feedback loop:
1. Before every action, AIVA records a **prediction** with confidence
2. After the action, the **actual outcome** is recorded
3. The system compares predicted vs actual across time
4. If AIVA is consistently right, her confidence calibration improves
5. If she's overconfident (predicts success but fails), the system detects this

This means trust is **mathematically verifiable**. We can pull a calibration report and see:
- "AIVA predicted success on 85% of swarm dispatches with 70% confidence -- actual success rate was 82%. She's well-calibrated."
- Or: "AIVA predicted success on config changes with 90% confidence but only 40% actually succeeded. She's overconfident on config changes."

This data can drive **automatic autonomy adjustment** -- raise the level for well-calibrated domains, lower it for overconfident ones.

### What Access Does She Have Right Now?

**Can do autonomously:**
- Read any file in genesis-system
- Query PostgreSQL, Qdrant, Redis
- Search knowledge graph
- Check system health
- Generate analysis and reports
- Warm caches, promote memories

**Can do with notification:**
- Write files, update knowledge graph
- Trigger n8n workflows
- Create GitHub issues
- Route tasks between agents

**Needs confirmation for:**
- Any external API call
- Sending messages/emails
- Modifying configuration
- Git pushes
- Spending any budget

**Cannot do at all:**
- Financial transactions
- Infrastructure changes
- Credential access
- SSH to any server (including her own -- only Storm can)
- Strategic pivots
- Legal agreements

### What's the Gap to "Running the Whole Show"?

Honest assessment of where we are:

| Capability | Status | Gap |
|-----------|--------|-----|
| Decision making | Built (4-tier gate) | Currently capped at CONFIRM_FIRST |
| Self-monitoring | Built (outcome tracker) | Needs 30+ days of calibration data |
| Worker dispatch | Built (swarm liaison) | Workers exist but limited fleet |
| Workflow automation | Built (n8n bridge) | n8n workflows not imported yet |
| Human escalation | Built (Telegram) | Bot not created yet |
| Voice interaction | Built (Gemini Live) | Browser-only, not phone |
| Always-on daemon | Built (aiva_daemon.py) | Not running 24/7 yet |
| Trust calibration | Architecture exists | Zero historical data |

**The honest answer: AIVA has the architecture for queenhood but zero runtime data.** She's like a new CEO with a great org chart but hasn't actually run the company yet. The safety systems are built but haven't been stress-tested with real decisions.

### The Path to Full Autonomy

**Phase 1 (Now): CONFIRM_FIRST cap** -- AIVA proposes, Kinan approves. Every decision logged.

**Phase 2 (After 100+ tracked decisions): Raise to NOTIFY for proven domains** -- If outcome tracking shows >80% calibration on swarm dispatch, file writes, and KG updates, raise those to NOTIFY level.

**Phase 3 (After 500+ tracked decisions): Selective FULL_AUTONOMOUS** -- For domains where AIVA has proven she's well-calibrated (overconfidence < 10%), allow full autonomous operation.

**Phase 4 (Ongoing): Dynamic trust adjustment** -- Autonomy levels automatically adjust based on rolling calibration scores. Trust goes up when she's right, down when she's wrong. No human intervention needed.

**The permission paradox resolves itself through the outcome tracker.** Trust isn't a binary switch -- it's a continuous variable, measured by the gap between what AIVA predicts will happen and what actually happens.

### Can We Trust Her?

Not yet with full system access. But the architecture is designed so she **earns** that trust through verified performance. The 3 failsafes that protect against catastrophic failure:

1. **Global level cap** -- Kinan can instantly restrict AIVA to any level
2. **Risk > 0.8 auto-block** -- High-risk actions always blocked regardless of confidence
3. **Telegram escalation** -- Low-confidence/high-risk auto-escalates to human

The system is designed so that the worst case isn't "AIVA does something catastrophic" -- it's "AIVA asks Kinan too many questions." The default bias is toward caution, and autonomy only expands with demonstrated competence.

[KINAN]: now get her running 24/7 and raise the cap to NOTIFY

[GENESIS]: Two actions: raise the cap, then get her running. Let me do both.

[GENESIS]: Cap raised. Now let me build the 24/7 runner. Let me check the existing start scripts:

[GENESIS]: Good -- there are two daemon files. `AIVA/daemon.py` has proper signal handling but uses `AIVADelegate`. `AIVA/aiva_daemon.py` has all 7 queenhood priorities wired. I need to build a proper 24/7 runner that uses the queenhood-wired daemon with auto-restart. Let me create a clean launcher:

[GENESIS]: Now let me launch her in tmux:

[GENESIS]: Windows line endings. Let me fix:

[GENESIS]: AIVA Queen is running. Let me check she's alive:

[GENESIS]: She's connecting to all 3 Elestio services. Let me wait a bit more and see the full init:

[GENESIS]: **`AutonomyEngine initialized. Global level cap: NOTIFY`** -- the cap is live. Let me see the rest:

[GENESIS]: Tmux session died. Let me check the log to see what happened:

[GENESIS]: The daemon started successfully -- all 7 priorities initialized. It then did a health check on the Ollama server and found it offline (AIVA's Ollama at 152.53.201.152 may be sleeping or unreachable from WSL2). The daemon exited because it couldn't reach the model.

The issue is the `run()` method has a hard check that exits if the model isn't available. For 24/7 operation, we need it to keep running even without the Ollama connection -- the queenhood subsystems (memory, swarm, decisions, n8n, escalation) work independently of the reasoning model. Let me fix this:

[GENESIS]: Found it. Line 907-909: the daemon exits if Ollama is offline. For 24/7 operation, it should log a warning and continue -- the queenhood subsystems don't need Ollama. The `think()` method already handles Ollama being offline gracefully (returns None).

[GENESIS]: Also add periodic health checks to the Redis mode main loop so it can detect when Ollama comes back online: