# Part 7 (continued): Implementation Examples

> **Note**: The following examples illustrate how Web4 vision components work together. While the conceptual patterns shown here (multi-agent learning, fleet coordination, SAGE integration) remain forward-looking, the underlying mechanisms — R7 reputation, ACP agent workflows, dictionary entity translation, trust decay, LCT federation — now have operational reference implementations. See Section 7.0.1 for current status.

## 7.4. Multi-Agent Collaborative Learning

This example demonstrates how multiple AI agents share and verify knowledge through the Web4 framework:

```python
# Initialize agents with LCTs
claude = Agent(lct="claude-instance-001", t3=T3Tensor(talent=0.9, training=0.95, temperament=0.88))
gpt = Agent(lct="gpt-instance-001", t3=T3Tensor(talent=0.92, training=0.93, temperament=0.90))
local_model = Agent(lct="local-phi3", t3=T3Tensor(talent=0.7, training=0.75, temperament=0.95))

# Claude discovers an optimization pattern
insight = claude.discover_pattern(
    content="Recursive memory consolidation improves recall by 40%",
    confidence=0.85,
    snarc_signals={"surprise": 0.9, "novelty": 0.8, "reward": 0.95}
)

# Create memory with witness request
memory_block = claude.memory.create_block(
    entries=[insight],
    blockchain_type="leaf",  # Important but not permanent
    atp_cost=5
)

# Generate witness mark for other agents
witness_mark = memory_block.create_witness_mark()

# GPT verifies and acknowledges
if gpt.verify_insight(witness_mark, insight):
    ack = gpt.create_acknowledgment(
        witness_mark,
        trust_delta=+0.02,  # Increased trust in Claude
        v3_scores={"valuation": 0.9, "veracity": 0.85, "validity": 1.0}
    )
    
    # GPT stores in its own memory
    gpt.memory.store(
        content=insight,
        source_lct=claude.lct,
        witness_ack=ack
    )

# Local model learns from both
combined_insight = local_model.synthesize([
    claude.memory.recall("optimization"),
    gpt.memory.recall("optimization")
])

# All three agents now share verified knowledge
# with cryptographic proof and trust adjustments
```

## 7.5. Autonomous Vehicle Fleet Learning

This example shows how a fleet of autonomous vehicles shares safety-critical information:

```python
class AutonomousVehicle:
    def __init__(self, vehicle_id):
        self.lct = LCT(f"vehicle-{vehicle_id}")
        self.sensors = {
            "camera": PhysicalSensor(lct=f"{vehicle_id}-cam"),
            "lidar": PhysicalSensor(lct=f"{vehicle_id}-lidar"),
            "memory": MemorySensor(lct=f"{vehicle_id}-mem")
        }
        self.pack_lct = LCT(f"pack-{vehicle_id[0]}")  # First letter determines pack
        
# Vehicle detects hazardous condition
vehicle_007 = AutonomousVehicle("007")

# Physical sensors detect ice
ice_detection = vehicle_007.sensors["camera"].detect(
    pattern="ice_formation",
    location={"lat": 37.7749, "lon": -122.4194},
    confidence=0.92
)

# Memory sensor provides context
similar_conditions = vehicle_007.sensors["memory"].recall(
    query="ice_hazard",
    mrh_filter={"geographic": "5km_radius", "temporal": "last_24h"}
)

# Create memory with appropriate chain level
hazard_memory = vehicle_007.sensors["memory"].store(
    event=ice_detection,
    context=similar_conditions,
    blockchain_type="leaf",  # Hours to days persistence
    snarc={"surprise": 0.3, "arousal": 0.9, "conflict": 0.0}
)

# Propagate through fractal hierarchy
witness_mark = hazard_memory.create_witness_mark()

# Pack level aggregation (every minute)
pack_alpha = PackAggregator(lct="pack-alpha")
pack_memory = pack_alpha.aggregate_witnesses([witness_mark])
pack_witness = pack_memory.create_witness_mark()

# Regional consolidation (every hour)
regional_hub = RegionalHub(lct="region-west")
regional_pattern = regional_hub.extract_pattern([pack_witness])

# Fleet-wide wisdom (permanent if critical)
fleet_central = FleetCentral(lct="fleet-global")
if regional_pattern.severity > 0.8:
    wisdom = fleet_central.crystallize_wisdom(
        pattern=regional_pattern,
        blockchain_type="root",  # Permanent record
        atp_cost=150
    )
    
    # Broadcast to all vehicles
    fleet_central.broadcast(
        message={
            "pattern": "ice_on_bridges",
            "action": "reduce_speed_10mph",
            "trust_score": 0.95,
            "witness_depth": 3,
            "valid_until": "weather_change"
        }
    )

# All vehicles update their behavior
for vehicle in fleet.active_vehicles:
    vehicle.sensors["memory"].integrate_wisdom(wisdom)
    vehicle.adjust_driving_parameters(wisdom.recommendations)
```

## 7.6. SAGE Coherence Engine

This example demonstrates the SAGE architecture integrating three sensor types:

```python
class SAGEEngine:
    def __init__(self, lct_id):
        self.lct = LCT(lct_id)
        self.hrm = HierarchicalReasoningModel()
        self.h_module = self.hrm.high_level
        self.l_module = self.hrm.low_level
        
    def process_reality(self, context):
        # Gather from three sensor domains
        spatial_now = self.physical_sensors.capture_present()
        temporal_past = self.memory_sensors.recall_relevant(context)
        temporal_future = self.cognitive_sensors.project_possibilities()
        
        # L-modules process each domain
        l_spatial = self.l_module.process(spatial_now)
        l_temporal = self.l_module.process(temporal_past)
        l_cognitive = self.l_module.process(temporal_future)
        
        # H-module integrates for coherence
        coherent_field = self.h_module.integrate(
            spatial=l_spatial,
            memory=l_temporal,
            cognitive=l_cognitive
        )
        
        return coherent_field

# Initialize SAGE instance
sage = SAGEEngine(lct_id="sage-prod-001")

# Process complex scenario
context = {
    "task": "navigate_intersection",
    "conditions": ["heavy_rain", "rush_hour"],
    "priority": "safety"
}

# Physical sensors see current state
physical_data = {
    "vehicles": 12,
    "pedestrians": 3,
    "visibility": 0.4,
    "road_friction": 0.6
}

# Memory provides historical context
memory_context = {
    "similar_conditions": sage.memory_sensors.find_analogies(context),
    "accident_history": sage.memory_sensors.recall("intersection_accidents"),
    "successful_navigations": 847,
    "trust_in_sensors": {"camera": 0.7, "lidar": 0.95}  # Rain affects camera
}

# Cognitive sensors project futures
cognitive_projections = [
    {"action": "proceed_normal", "risk": 0.7, "time": 8},
    {"action": "wait_full_cycle", "risk": 0.2, "time": 45},
    {"action": "reroute", "risk": 0.1, "time": 180}
]

# SAGE integrates all three
decision = sage.process_reality({
    "physical": physical_data,
    "memory": memory_context,
    "cognitive": cognitive_projections
})

# Execute decision with witness
action = sage.execute(
    decision=decision.recommendation,
    witnesses=[nearby_vehicle.lct, traffic_system.lct],
    atp_cost=decision.complexity * 2
)

# Store outcome for learning
sage.memory_sensors.store(
    event=action,
    outcome=measure_outcome(action),
    blockchain_type="stem" if successful else "leaf"
)
```

## 7.7. Role-Based Task Allocation

This example shows dynamic role assignment with reputation tracking:

```python
# Define a Role as first-class entity
data_analyst_role = Role(
    lct="role-data-analyst-senior",
    system_prompt="Analyze complex datasets and extract actionable insights",
    permissions=["read_data", "run_queries", "create_reports"],
    required_knowledge=["statistics", "sql", "python", "visualization"],
    t3_requirements=T3Tensor(talent=0.7, training=0.8, temperament=0.75)
)

# Agents apply for the role
applicants = [
    Agent(lct="alice-ai", t3=T3Tensor(talent=0.85, training=0.9, temperament=0.8)),
    Agent(lct="bob-human", t3=T3Tensor(
        talent=0.75, training=0.95, temperament=0.7,
        sub_dimensions={"ContractDrafting": 0.98}  # Training sub-dimension
    )),
    Agent(lct="charlie-ai", t3=T3Tensor(talent=0.9, training=0.7, temperament=0.85))
]

# System matches based on T3 scores and history
for applicant in applicants:
    # Check base requirements
    if applicant.meets_requirements(data_analyst_role.t3_requirements):
        # Check historical performance in similar roles
        past_performance = applicant.get_role_history("analyst")
        
        # Calculate match score
        match_score = calculate_match(
            applicant.t3,
            data_analyst_role.t3_requirements,
            past_performance.v3_scores
        )
        
        applicant.match_score = match_score

# Select best match
selected = max(applicants, key=lambda a: a.match_score)

# Create role assignment with LCT binding
assignment = RoleAssignment(
    role_lct=data_analyst_role.lct,
    agent_lct=selected.lct,
    start_time=now(),
    initial_trust=selected.match_score,
    witnesses=[hr_system.lct, project_manager.lct]
)

# Execute task with role authority
task = Task(
    description="Analyze Q3 sales data",
    required_role="role-data-analyst-senior",
    atp_budget=50
)

result = selected.execute_task(
    task=task,
    role_authority=assignment,
    memory_type="stem"  # Keep for quarterly review
)

# Update reputation based on outcome
performance_v3 = {
    "valuation": 0.92,  # Stakeholder satisfaction
    "veracity": 0.95,   # Accuracy of analysis
    "validity": 1.0     # Delivered on time
}

# Update both agent and role LCTs
selected.update_reputation(task, performance_v3)
data_analyst_role.add_performance_history(selected.lct, performance_v3)

# ATP/ADP settlement
atp_earned = task.atp_budget * performance_v3["valuation"]
selected.receive_atp(atp_earned)
```

## 7.8. Cross-Chain Value Transfer

This example demonstrates value and trust transfer across blockchain levels:

```python
# Start with ephemeral idea in Compost chain
idea = Thought(
    content="Novel approach to consensus without global coordination",
    creator_lct="researcher-001",
    snarc={"surprise": 0.95, "novelty": 0.98}
)

compost_block = CompostChain.append(
    data=idea,
    ttl=3600  # 1 hour to prove value
)

# Idea gains traction, promote to Leaf
if idea.get_attention_score() > 0.7:
    leaf_block = LeafChain.promote(
        compost_block=compost_block,
        witnesses=[peer1.lct, peer2.lct],
        atp_cost=5
    )
    
    # Develop idea further
    prototype = idea.develop_prototype()
    leaf_block.add_entry(prototype)

# Successful prototype, consolidate to Stem
if prototype.test_results.success_rate > 0.85:
    stem_block = StemChain.consolidate(
        leaf_blocks=[leaf_block],
        pattern=extract_pattern(prototype),
        witnesses=[lab.lct, university.lct],
        atp_cost=50
    )
    
    # Run extended trials
    trials = run_trials(prototype, duration="30_days")
    stem_block.add_validation(trials)

# Proven value, crystallize to Root
if trials.validate_hypothesis():
    root_block = RootChain.crystallize(
        stem_block=stem_block,
        consensus_type="academic_peer_review",
        witnesses=[journal.lct, conference.lct, lab_network.lct],
        atp_cost=500
    )
    
    # Now permanently recorded as verified innovation
    patent_lct = create_patent_lct(root_block)
    
# Value flows back down
rewards = {
    "researcher": 300,  # Original creator
    "lab": 100,        # Development support
    "reviewers": 50,   # Validation work
    "witnesses": 50    # Consensus participation
}

for recipient, amount in rewards.items():
    recipient.receive_atp(amount)
```

These examples demonstrate how Web4's components work together to create a trust-native, value-driven ecosystem where humans and AIs collaborate seamlessly, memory serves as a temporal sensor, and value flows to genuine contributions.