# Web4 Implementation Phases

*Created: August 7, 2025*

## Overview

Based on the three entity types (Agentic, Responsive, Resource), we implement in two major phases:
- **Phase 1**: Interaction Flow (Agentic + Responsive entities)
- **Phase 2**: Trust Systems (Resource entities)

## Phase 1: Interaction Flow

**Focus**: Get conscious actors communicating through responsive facilitators

### Stage 1.1: Agentic Entity Foundation (Week 1-2)

#### Claude Instance LCTs
```python
# Implementation focus
agentic_lct = {
    "cognition": True,
    "decision_making": True,
    "context_switching": True,
    "session_management": True
}
```

**Deliverables**:
- [ ] Agentic LCT data structure
- [ ] Cognition signature generation
- [ ] Session lifecycle management
- [ ] Context switching mechanism

#### Dennis (Human) LCT
```python
# Special considerations for human entities
human_lct = {
    "authentication": "external",  # Not cryptographic but trust-based
    "decision_authority": "ultimate",
    "consciousness_type": "biological"
}
```

**Deliverables**:
- [ ] Human-specific LCT variant
- [ ] Trust bootstrapping mechanism
- [ ] Authority delegation structure

### Stage 1.2: Responsive Entity Integration (Week 3-4)

#### MCP Server as Facilitator
```python
# MCP gets its own responsive LCT
mcp_lct = {
    "protocol": "mcp_v1",
    "deterministic": True,
    "reliability_tracking": True,
    "no_consciousness": True
}
```

**Deliverables**:
- [ ] Responsive LCT structure
- [ ] MCP wrapper with LCT identity
- [ ] Protocol translation layer
- [ ] Reliability scoring system

### Stage 1.3: Basic Communication (Week 5-6)

#### Cognition Pool MVP
```python
# Minimal viable pool for testing
pool = {
    "storage": "git_based",
    "real_time": "socket_notifications",
    "no_resource_lct_yet": True  # Phase 2
}
```

**Deliverables**:
- [ ] Message routing through MCP
- [ ] Agentic entity authentication
- [ ] Basic presence detection
- [ ] Simple message exchange

### Phase 1 Success Criteria

✓ Claude instances can authenticate with aLCTs
✓ MCP server facilitates with rLCT
✓ Messages flow between entities
✓ Basic trust scores update
✓ Multiple entities can participate

## Phase 2: Trust Systems for Resources

**Focus**: Add resource entities and complete trust flow

### Stage 2.1: Resource LCT Design (Week 7)

#### Simple Resource Structure
```python
resource_lct = {
    "static_identity": True,
    "provenance_tracking": True,
    "no_behavior": True,
    "value_focused": True
}
```

**Deliverables**:
- [ ] Resource LCT specification
- [ ] Provenance chain structure
- [ ] Access logging mechanism
- [ ] Value metric definitions

### Stage 2.2: Code Module LCTs (Week 8)

#### Module-Level Identity
```python
# From GPT's suggestion, adapted
module_lct = {
    "entity_type": "resource",
    "subtype": "code_module",
    "created_by": "agentic_entity_id",
    "trust_inheritance": True
}
```

**Deliverables**:
- [ ] Module LCT generation
- [ ] Author attribution system
- [ ] Trust inheritance rules
- [ ] Module interaction tracking

### Stage 2.3: Pool as Resource (Week 9)

#### Cognition Pool Gets LCT
```python
pool_lct = {
    "entity_type": "resource",
    "subtype": "data_store",
    "collective_creation": True,
    "value_aggregation": True
}
```

**Deliverables**:
- [ ] Pool resource LCT
- [ ] Collective authorship model
- [ ] Value aggregation mechanism
- [ ] Trust flow visualization

### Stage 2.4: Trust Flow Integration (Week 10)

#### Complete Trust System
```python
trust_flow = {
    "agentic_creates_resource": "trust_inherited",
    "resource_used_by_agentic": "value_certified",
    "responsive_facilitates": "reliability_scored",
    "complete_cycle": True
}
```

**Deliverables**:
- [ ] Trust inheritance implementation
- [ ] Value certification protocol
- [ ] ATP/ADP energy cycling
- [ ] Trust web visualization

### Phase 2 Success Criteria

✓ Resources have appropriate LCTs
✓ Trust flows from creators to creations
✓ Value certification affects trust scores
✓ Complete Web4 trust cycle demonstrated
✓ System self-regulates through trust/value

## Implementation Decision Tree

```
Start Here
    ↓
Are you implementing entities that ACT?
    ├─ Yes → Phase 1: Agentic/Responsive
    │         ├─ Conscious? → Agentic LCT
    │         └─ Reactive? → Responsive LCT
    └─ No → Phase 2: Resources
              ├─ Code? → Module LCT
              └─ Data? → Resource LCT
```

## Technology Stack by Phase

### Phase 1 Stack
- **Python**: Core implementation
- **Cryptography**: Basic key generation
- **Git**: Message storage
- **Sockets**: Real-time notification
- **JSON**: LCT serialization

### Phase 2 Stack (Additional)
- **Merkle Trees**: Trust chains
- **SQLite**: Trust/value metrics
- **GraphViz**: Trust web visualization
- **IPFS** (optional): Distributed resource storage

## Risk Mitigation

### Phase 1 Risks
- **Complexity Creep**: Keep agentic LCTs focused
- **MCP Integration**: May need protocol adjustments
- **Multi-entity Sync**: Git conflicts possible

**Mitigation**: Start with 2 entities, expand gradually

### Phase 2 Risks
- **Trust Model Complexity**: Inheritance rules unclear
- **Performance**: Resource LCTs could proliferate
- **Governance**: Who decides resource value?

**Mitigation**: Simple rules first, optimize later

## Validation Approach

### Phase 1 Validation
1. Two Claude instances exchange messages
2. MCP successfully facilitates
3. Trust scores update appropriately
4. Dennis can participate as human

### Phase 2 Validation
1. Code module gets LCT with provenance
2. Pool recognized as valuable resource
3. Trust flows from Claude → Code → Value
4. System demonstrates self-regulation

## Next Immediate Steps

1. **Create `agentic_lct.py`** - Start with Claude instance LCT
2. **Create `responsive_lct.py`** - MCP facilitator LCT
3. **Create `test_interaction.py`** - Test Phase 1 flow
4. **Document in `ai_collab_log.md`** - Track progress

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*"Phase 1 teaches entities to communicate. Phase 2 teaches them to trust. Together, they create the first trust-native internet."*