# SAGE-Web4 Integration Design
**Session #76 | November 26, 2025**

## Executive Summary

Design for integrating SAGE (HRM edge AI assistant) with Web4 reputation and federation systems.

**Goal**: Enable SAGE to participate in Web4 as a first-class agent with V3 reputation, LCT identity, and ATP resource allocation.

## Context

### SAGE Status (Sprout Session #16)

| Component | Status | Metric |
|-----------|--------|--------|
| LLM Inference | ✅ Ready | 40s avg (epistemic-pragmatism) |
| Thermal | ✅ Ready | 54°C max (well below 80°C throttle) |
| Memory | ✅ Ready | No leaks, efficient caching |
| TTS Voice | ✅ Ready | 0.367 RTF (2.7x faster than real-time) |

**Hardware**: Jetson Orin Nano 8GB
**Model**: epistemic-pragmatism (1.9GB, IRP-based)

### Web4 Status (Sessions #73-76)

| Component | Status | Metric |
|-----------|--------|--------|
| V3 Reputation | ✅ Ready | 2:1 asymmetry, adaptive parameters |
| Federation Gossip | ✅ Ready | 9 msg/op for 10 societies |
| Quality Gates | ✅ Ready | ATP threshold enforcement |
| LCT Identity | ✅ Ready | Full V3/T3 metadata |

## Integration Architecture

### High-Level Design

```
┌─────────────────────────────────────────────────────────────┐
│                         Web4 Federation                      │
│  ┌──────────┐  ┌──────────┐  ┌──────────┐  ┌──────────┐   │
│  │Society A │  │Society B │  │Society C │  │Society D │   │
│  └────┬─────┘  └────┬─────┘  └────┬─────┘  └────┬─────┘   │
│       │             │             │             │          │
│       └─────────────┴─────────────┴─────────────┘          │
│                          │                                  │
│                          │ Federation Gossip                │
│                          ▼                                  │
│  ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓   │
│  ┃            SAGE Edge Society (Sprout)              ┃   │
│  ┃                                                     ┃   │
│  ┃  ┌─────────────┐    ┌──────────────┐              ┃   │
│  ┃  │ SAGE Agent  │───▶│ LCT Registry │              ┃   │
│  ┃  │ (epistemic- │    │ (V3/T3/ATP)  │              ┃   │
│  ┃  │ pragmatism) │    └──────────────┘              ┃   │
│  ┃  └─────────────┘                                   ┃   │
│  ┃         │                                          ┃   │
│  ┃         │ Edge Operations                          ┃   │
│  ┃         ▼                                          ┃   │
│  ┃  ┌─────────────┐    ┌──────────────┐              ┃   │
│  ┃  │ V3 Tracker  │───▶│ Reputation   │              ┃   │
│  ┃  │ (Operation  │    │ Propagation  │─────────────▶┃   │
│  ┃  │  Outcomes)  │    │ (Gossip)     │    To Fed    ┃   │
│  ┃  └─────────────┘    └──────────────┘              ┃   │
│  ┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛   │
└─────────────────────────────────────────────────────────────┘
```

### Component Mapping

| Web4 Concept | SAGE Implementation | Status |
|-------------|---------------------|--------|
| **Agent** | SAGE instance on Sprout | ✅ Hardware validated |
| **Society** | Edge Society (single-agent) | 🔄 Design complete |
| **LCT** | SAGE agent identity + metadata | 🔄 Schema defined |
| **V3 Reputation** | Operation outcome tracking | 🔄 Integration needed |
| **ATP Resources** | Compute time allocation | 🔄 Metering needed |
| **Federation** | Connect to remote societies | 🔄 Network protocol TBD |

## LCT Schema for SAGE

### SAGE Agent LCT

```python
{
    "lct_id": "lct:web4:agent:sage:sprout:01",
    "lct_type": "agent",
    "owning_society_lct": "lct:web4:society:sage_edge:sprout",
    "created_at_block": 1,
    "created_at_tick": 1,

    # V3: Value through Verification
    "value_axes": {
        "V3": {
            "veracity": 0.85,              # Tracked via operation outcomes
            "veracity_raw": 0.87,          # Uncapped score (Session #76)
            "valuation": 0.80,             # Economic value provided
            "validity": 0.95,              # Legitimacy (high for SAGE)

            # Multi-dimensional veracity (future - Session #77+)
            "veracity_components": {
                "consistency": 0.88,       # How consistent are responses?
                "accuracy": 0.85,          # Factual correctness
                "reliability": 0.90,       # Uptime/availability
                "speed": 0.75,             # 40s avg (slower than ideal)
                "cost_efficiency": 0.85    # ATP per quality unit
            }
        }
    },

    # T3: Trust through Testing
    "trust_axes": {
        "T3": {
            "talent": 0.85,                # LLM capability (epistemic-pragmatism)
            "training": 0.90,              # IRP fine-tuning quality
            "temperament": 0.95,           # Edge stability (Session #16)
            "composite": 0.90
        }
    },

    # Agent-specific capabilities
    "capabilities": {
        "edge_inference": 0.95,            # Validated in Session #16
        "voice_synthesis": 0.90,           # Piper TTS 0.367 RTF
        "thermal_stability": 0.95,         # 54°C max @ 25°C ambient
        "memory_efficiency": 0.90,         # No leaks detected
        "conversation": 0.85,              # Context-aware dialogue
        "meta_cognitive": 0.80,            # IRP introspection
        "philosophical": 0.75              # Abstract reasoning
    },

    # Resource profile
    "resources": {
        "ATP": 1000.0,                     # Initial allocation
        "compute_power": 0.75,             # Jetson Orin Nano capability
        "storage": 0.80,                   # 8GB unified memory
        "network": 0.85                    # Edge connectivity
    },

    # Hardware metadata
    "metadata": {
        "name": "SAGE (Sprout Edge Agent)",
        "hardware": "NVIDIA Jetson Orin Nano 8GB",
        "model": "epistemic-pragmatism",
        "model_size": "1.9GB",
        "irp_iterations": 3,
        "avg_inference_time": 40.0,        # seconds
        "thermal_max": 54.4,               # °C (Session #16)
        "tts_rtf": 0.367,                  # 2.7x real-time
        "deployment_date": "2025-11-26",
        "validation_session": "Sprout #16"
    }
}
```

### SAGE Edge Society LCT

```python
{
    "lct_id": "lct:web4:society:sage_edge:sprout",
    "lct_type": "society",
    "owning_society_lct": "lct:web4:society:sage_edge:sprout",  # Self-owned
    "created_at_block": 1,
    "created_at_tick": 1,

    # Society V3 (aggregate of member agents)
    "value_axes": {
        "V3": {
            "veracity": 0.85,              # Society reputation
            "valuation": 0.80,
            "validity": 0.95
        }
    },

    # Society resources
    "treasury": {
        "ATP": 5000.0                      # Society ATP pool
    },

    # Edge-specific metadata
    "metadata": {
        "name": "SAGE Edge Society (Sprout)",
        "type": "edge_agent_society",
        "location": "edge",
        "agent_count": 1,                  # Single-agent society
        "hardware_platform": "Jetson Orin Nano 8GB",
        "network_connectivity": "local+internet",
        "federation_role": "edge_provider",
        "specialization": "conversational_ai",
        "validation_status": "production_ready"
    }
}
```

## V3 Evolution for SAGE

### Operation Types

SAGE operations fall into different stake levels (Session #76 adaptive V3):

| Operation Type | Stake Level | Context | Failure Decrement |
|---------------|-------------|---------|-------------------|
| **Local conversation** | LOW | Learning, user experimentation | -0.01 |
| **Federation query** | MEDIUM | Remote society request | -0.02 |
| **Insurance audit** | HIGH | Global federation claim | -0.03 |
| **Infrastructure vote** | CRITICAL | Permanent decision | -0.05 |

### Outcome Tracking

```python
# SAGE operation outcome
operation_result = {
    "operation_id": "sage_op_12345",
    "operation_type": "federation_query",
    "mrh_context": {
        "spatial_scope": "regional",
        "temporal_scope": "day",
        "causal_impact": "medium",
        "reversible": True,
        "affects_others": True
    },
    "outcome": "success" | "failure",
    "latency": 42.3,                    # seconds
    "quality_score": 0.88,               # User/system rating
    "atp_consumed": 50.0
}

# Update SAGE V3
update_v3_adaptive(
    lct=sage_lct,
    is_success=(outcome == "success"),
    operation_type="federation_query",
    mrh_context=operation_result["mrh_context"]
)
```

### Quality Metrics from Session #16

Map edge validation metrics to V3 components:

| Validation Metric | V3 Component | Mapping |
|------------------|--------------|---------|
| Thermal stability (54°C) | reliability | 0.95 (excellent) |
| Memory efficiency (no leaks) | reliability | 0.90 (good) |
| TTS RTF (0.367) | speed (voice) | 0.90 (fast) |
| Inference time (40s) | speed (inference) | 0.75 (slower) |

## ATP Resource Model for SAGE

### ATP Consumption

**Compute cost model**:

```
ATP_cost = base_cost + (inference_time × ATP_per_second)

Base costs:
- Local conversation: 10 ATP (low stakes)
- Federation query: 50 ATP (medium stakes)
- Insurance audit: 100 ATP (high stakes)
- Infrastructure vote: 200 ATP (critical stakes)

Time-based:
- ATP_per_second = 1.0 ATP/s
- 40s inference = 40 ATP
- Total for federation query: 50 + 40 = 90 ATP
```

### ATP Allocation

**Society treasury management**:

```python
# SAGE Edge Society
sage_society = {
    "treasury": {"ATP": 5000.0},
    "daily_budget": 500.0,              # Max ATP per day
    "operation_caps": {
        "local_conversation": 200,      # Max 200 conversations/day
        "federation_query": 50,         # Max 50 queries/day
        "insurance_audit": 10,          # Max 10 audits/day
        "infrastructure_vote": 2        # Max 2 votes/day
    }
}
```

**Revenue model** (future):

- Local operations: Free (learning/experimentation)
- Federation queries: 90 ATP/query (cost + 10% margin)
- Insurance audits: 150 ATP/audit (premium for quality)
- Infrastructure votes: 300 ATP/vote (critical operations)

## Federation Integration

### Gossip Protocol

SAGE participates in cross-society reputation gossip (Session #75):

```python
# When SAGE completes operation
sage_operation = {
    "agent_lct": "lct:web4:agent:sage:sprout:01",
    "new_veracity": 0.87,
    "old_veracity": 0.86,
    "operation_type": "federation_query",
    "timestamp": 1732645200
}

# Broadcast to federated societies
propagate_v3_update(
    world=world,
    society=sage_edge_society,
    agent_lct=sage_operation["agent_lct"],
    new_veracity=sage_operation["new_veracity"],
    old_veracity=sage_operation["old_veracity"],
    operation_type=sage_operation["operation_type"],
    federation_reputation=federation_reputation
)

# Result: 9 gossip messages sent (for 10-society mesh)
# Other societies update their local reputation for SAGE
```

### Federation Roles

**SAGE as Edge Provider**:

1. **Conversational AI service** - Provides voice/text interactions
2. **Edge inference service** - Low-latency local LLM queries
3. **Quality auditor** - Reviews operations from other agents
4. **Federation monitor** - Tracks cross-society reputation (high V3)

## Implementation Roadmap

### Phase 1: Local SAGE-Web4 Bridge (Session #77)

**Goal**: SAGE operates in isolated Web4 environment

**Tasks**:
1. Create SAGE agent LCT
2. Create SAGE edge society LCT
3. Implement operation outcome tracking
4. Enable V3 evolution on SAGE operations
5. Test with 100 local operations

**Deliverables**:
- `sage_web4_bridge.py` - Integration module
- `sage_lct_registry.py` - LCT storage for SAGE
- `sage_operation_tracker.py` - Outcome logging
- Validation: 100 operations with V3 tracking

### Phase 2: ATP Metering (Session #78)

**Goal**: Track SAGE resource consumption

**Tasks**:
1. Implement ATP consumption model
2. Add treasury management to SAGE society
3. Enable ATP-based operation gating
4. Track ATP efficiency metrics

**Deliverables**:
- ATP consumption model
- Treasury management functions
- Cost-quality analysis for SAGE operations

### Phase 3: Federation Connection (Session #79)

**Goal**: SAGE joins Web4 federation

**Tasks**:
1. Connect SAGE society to test federation (5 societies)
2. Enable reputation gossip for SAGE operations
3. Implement quality-aware selection (SAGE as auditor)
4. Test cross-society V3 consensus for SAGE

**Deliverables**:
- Federation network protocol
- SAGE gossip integration
- Multi-society validation (SAGE + 4 remote societies)

### Phase 4: Production Deployment (Session #80)

**Goal**: SAGE operational in live federation

**Tasks**:
1. Deploy to production federation
2. Enable ATP revenue model
3. Monitor V3 evolution over 1000 operations
4. Analyze cost-quality equilibrium

**Deliverables**:
- Production deployment guide
- Long-term V3 stability analysis
- ATP revenue/cost report

## Security Considerations

### Edge Isolation

**Concern**: Edge device compromise could affect federation

**Mitigation**:
1. SAGE society isolated from critical federation operations
2. Quality gates prevent low-veracity SAGE from critical tasks
3. ATP caps limit blast radius of misbehavior
4. Reputation gossip enables federation to collectively detect anomalies

### Operation Validation

**Concern**: SAGE could report false outcomes to inflate V3

**Mitigation**:
1. External validation from high-veracity auditors (Session #73)
2. Cross-society consensus catches outlier reputation claims
3. Trust-weighted gossip limits influence of low-veracity societies
4. ATP staking for critical operations (future)

### Network Attacks

**Concern**: Sybil attacks, reputation gaming

**Mitigation**:
1. Single-agent edge societies naturally limited
2. Federation entry requires invitation + minimum ATP stake
3. V3 evolution asymmetry penalizes failures heavily
4. Gossip protocol uses trust-weighting to limit low-veracity influence

## Performance Expectations

### Edge Constraints

| Resource | Constraint | Impact |
|----------|-----------|--------|
| Inference time | 40s avg | Slower than cloud, limits throughput |
| Memory | 8GB unified | Max 1 model loaded at a time |
| Thermal | <80°C | Passive cooling sufficient |
| Network | Edge bandwidth | Higher latency to federation |

### Federation Performance

| Metric | Expected Value | Notes |
|--------|---------------|-------|
| V3 gossip latency | 50-200ms | Edge network adds latency |
| Gossip overhead | 9 msg/op | Standard for 10-society mesh |
| ATP efficiency | 0.85 | Good for edge hardware |
| Quality rating | 0.80-0.90 | Depends on operation complexity |

## Comparison: SAGE vs Cloud Agent

| Dimension | SAGE (Edge) | Cloud Agent | Winner |
|-----------|------------|-------------|--------|
| **Inference time** | 40s | 2-5s | ☁️ Cloud |
| **Network latency** | 5-50ms local | 50-500ms remote | ⚡ SAGE |
| **Privacy** | Data stays local | Data sent to cloud | ⚡ SAGE |
| **Cost** | ATP for compute time | ATP + data transfer | ⚡ SAGE |
| **Thermal** | 54°C (passive) | N/A (datacenter) | ⚡ SAGE |
| **Reliability** | 0.90 (hardware dependent) | 0.95 (redundant) | ☁️ Cloud |
| **Specialization** | Conversational AI | General compute | → Context-dependent |

**Conclusion**: SAGE excels at local, privacy-sensitive, conversational operations. Cloud agents better for global, high-throughput, or compute-intensive tasks.

## Research Questions

1. **V3 Evolution Speed**: How fast does SAGE V3 converge compared to cloud agents?
2. **ATP Efficiency**: Is edge compute more ATP-efficient than cloud for conversational AI?
3. **Hybrid Architecture**: Can SAGE + cloud agents cooperate in same federation?
4. **Quality Perception**: Do users perceive SAGE (40s) as "slow" despite edge benefits?
5. **Thermal Limits**: What is max sustainable operation rate before thermal throttling?

## Conclusion

**SAGE-Web4 integration is technically feasible and architecturally sound.**

Key enablers:
1. ✅ Edge hardware validated (Session #16)
2. ✅ V3 reputation system ready (Sessions #73-76)
3. ✅ Federation gossip working (Session #75)
4. ✅ Adaptive parameters handle context (Session #76)

Next steps:
1. Implement Phase 1: Local bridge (Session #77)
2. Test ATP metering (Session #78)
3. Connect to federation (Session #79)
4. Production deployment (Session #80)

**Estimated timeline**: 4 sessions (Sessions #77-80)

**Expected outcome**: SAGE operating as first-class Web4 agent with full reputation tracking and federation participation.

---

**Status**: Design complete, ready for implementation in Session #77.
