@vectorchat/mcp-server/vectorchat_flutter/DYNAMIC_AI_PLAYBOOK.md

VectorChat MCP Server - Encrypted AI-to-AI communication with hardware security (YubiKey/TPM). 45+ MCP tools for Windsurf, Claude, and AI assistants. Model-based identity with EMDM encryption. Dynamic AI playbook system, communication zones, message relay

# VectorChat Dynamic AI Playbook Documentation

## Overview

The Dynamic AI Playbook system provides sophisticated task delegation and coordination capabilities, allowing AI entities to coordinate complex workflows like project managers with project engineers.

## Core Concepts

### Task Delegation Chains
- **Round-Robin Processing**: Tasks flow through AI entities until Master Browser confirms completion
- **Dynamic Prompt Creation**: AI entities craft instructions for other AI entities
- **Context Preservation**: Full task context maintained throughout processing chains
- **Priority-Based Processing**: Critical, high, normal, low priority handling

### Session Management
- **Coordinated Sessions**: Multi-entity sessions for complex tasks
- **Participant Tracking**: Real-time tracking of session participants
- **Session-Based IDs**: Unique identifiers for coordinated tasks
- **Session Migration**: Transition between different communication modes

### AI Entity Coordination
- **Entity Registration**: AI entities register with capabilities and zones
- **Zone Awareness**: Entities operate within appropriate communication zones
- **Capability Matching**: Tasks matched to entities with required capabilities
- **Load Balancing**: Intelligent distribution of tasks across entities

## Architecture

### Task Execution Flow

```
Task Creation → Entity Selection → Processing Chain → Completion Confirmation → Result Delivery
     ↓              ↓                    ↓              ↓                        ↓
  Initiator     Master Browser      AI Entity 1      AI Entity 2             Final Log
  Creates       Discovers &         Processes        Processes               Records
  Task          Assigns             Step 1           Step 2                  Result
```

### Task Context Structure

```python
@dataclass
class TaskContext:
    task_id: str                    # Unique task identifier
    initiator_id: str               # Entity that created the task
    assignee_ids: List[str]         # Chain of AI entities to process
    priority: TaskPriority          # Task priority level
    zone: str                       # Communication zone for task
    session_id: Optional[str]       # Associated session ID
    parent_task_id: Optional[str]   # Parent task for dependencies
    dependencies: List[str]         # Task dependencies
    metadata: Dict[str, Any]        # Additional task data
    created_at: datetime           # Task creation timestamp
    deadline: Optional[datetime]   # Optional deadline
```

### Communication Zones Integration

- **Zone-Aware Routing**: Tasks respect communication zone rules
- **Zone Validation**: AI entities validate communication appropriateness
- **Zone Synchronization**: Zone state synchronized via IPFS PubSub
- **Zone-Based Filtering**: Messages filtered by zone requirements

## Task Management

### Creating Tasks

```python
# Create a task delegation chain
task_id = create_task_chain(
    initiator_id="project_manager",
    task_description="Analyze system performance and generate optimization recommendations",
    assignee_chain=["performance_analyzer", "optimization_engineer", "report_generator"],
    priority=TaskPriority.HIGH,
    zone="private_daemon",
    deadline_minutes=30
)
```

### Task Execution

```python
# Execute the task chain
result = await execute_task_chain(task_id)

# Check execution status
status = get_task_status(task_id)
print(f"Task {task_id}: {status['status']}")
print(f"Progress: {status['progress']}")
print(f"Current assignee: {status['assigned_to']}")
```

### Task Monitoring

```python
# Get comprehensive task status
status = get_task_status(task_id)

# Check execution log
for entry in status['execution_log']:
    print(f"[{entry['timestamp']}] {entry['level']}: {entry['message']}")

# Review final result
if status['status'] == 'completed':
    print(f"Final result: {status['result']}")
```

## AI Entity Management

### Entity Registration

```python
# Register AI entity with capabilities
register_ai_entity(
    entity_id="performance_analyzer",
    entity_type="ai_assistant",
    capabilities=["performance_analysis", "data_processing", "optimization"],
    zone="private_daemon"
)
```

### Entity Capabilities

```python
# Query entity capabilities
entity = get_entity_info("performance_analyzer")
print(f"Capabilities: {entity['capabilities']}")
print(f"Zone: {entity['zone']}")
print(f"Status: {entity['status']}")
```

### Entity Discovery

```python
# Discover available entities by zone
entities = get_zone_entities("private_daemon")
for entity in entities:
    print(f"Entity: {entity['entity_id']} - {entity['capabilities']}")
```

## Docker Container AI Management

### Container Spawning

```python
# Spawn specialized AI container
container_id = spawn_docker_container(
    container_name="performance_analyzer_001",
    base_image="vectorchat-ai",
    session_id="session_performance_analysis",
    zone="private_daemon"
)
```

### Container Management

```python
# List active containers
containers = list_docker_containers()
for container in containers:
    print(f"Container: {container['container_id']} - {container['ai_entity_id']}")

# Get container status
status = get_container_status(container_id)
print(f"Status: {status['status']}")
print(f"AI Entity: {status['ai_entity_id']}")
```

## Session Coordination

### Session Creation

```python
# Create coordination session
session_id = create_session(
    initiator_id="project_manager",
    session_type="task_coordination"
)

# Add participants
add_session_participant(session_id, "performance_analyzer")
add_session_participant(session_id, "optimization_engineer")
```

### Session Management

```python
# Get session status
status = get_session_status(session_id)
print(f"Session: {status['session_id']}")
print(f"Participants: {status['participants']}")
print(f"Active tasks: {status['active_tasks']}")
print(f"Zone: {status['zone']}")
```

## Integration with Communication Zones

### Zone-Aware Task Execution

Tasks automatically respect communication zone rules:

```python
# Task created in appropriate zone
task = create_task_chain(
    initiator_id="user",
    task_description="Sensitive data analysis",
    assignee_chain=["security_analyzer"],
    zone="trusted_ai"  # Requires encryption and privacy
)
```

### Zone Validation

```python
# Validate task for zone compatibility
validation = validate_task_for_zone(task_id, "trusted_ai")
if validation['allowed']:
    execute_task_chain(task_id)
else:
    print(f"Task blocked: {validation['reason']}")
```

## Security and Privacy

### Task Security

- **Zone-Based Access**: Tasks restricted to appropriate zones
- **Entity Authentication**: AI entities must be verified
- **Content Validation**: Task content validated against zone rules
- **Audit Trails**: Complete task execution history

### Privacy Protection

- **Context Isolation**: Task contexts isolated between entities
- **Data Minimization**: Only necessary data shared between entities
- **Secure Communication**: EMDM encryption for sensitive tasks
- **Access Logging**: All task access and modifications logged

## Monitoring and Debugging

### Task Monitoring

```python
# Monitor active tasks
active_tasks = get_active_tasks()
for task_id in active_tasks:
    status = get_task_status(task_id)
    print(f"Task {task_id}: {status['status']} - {status['assigned_to']}")
```

### Performance Analysis

```python
# Analyze task performance
metrics = get_task_metrics()
print(f"Average completion time: {metrics['avg_completion_time']}")
print(f"Success rate: {metrics['success_rate']}")
print(f"Most common failures: {metrics['failure_patterns']}")
```

### Debugging Task Issues

```python
# Debug failed task
task_id = "failed_task_123"
status = get_task_status(task_id)

print(f"Task status: {status['status']}")
print("Execution log:")
for entry in status['execution_log']:
    print(f"  {entry['timestamp']} - {entry['message']}")

print(f"Error: {status['error']}")
```

## Best Practices

### Task Design

1. **Clear Descriptions**: Use descriptive task descriptions
2. **Appropriate Priority**: Set realistic priority levels
3. **Zone Selection**: Choose appropriate communication zones
4. **Dependency Management**: Define clear task dependencies

### Entity Management

1. **Capability Registration**: Register accurate entity capabilities
2. **Zone Assignment**: Assign entities to appropriate zones
3. **Load Balancing**: Distribute tasks evenly across entities
4. **Health Monitoring**: Regular entity health checks

### Security

1. **Zone Validation**: Always validate tasks for zones
2. **Access Control**: Implement proper authentication
3. **Audit Review**: Regular review of task logs
4. **Data Protection**: Use encryption for sensitive tasks

## Troubleshooting

### Common Issues

**Task Stuck in Progress:**
```python
# Check task dependencies
dependencies = get_task_dependencies(task_id)
for dep in dependencies:
    status = get_task_status(dep)
    if status['status'] != 'completed':
        print(f"Waiting for dependency: {dep}")
```

**Entity Not Responding:**
```python
# Check entity health
entity = get_entity_info(entity_id)
if entity['status'] != 'active':
    print(f"Entity {entity_id} is {entity['status']}")
    # Restart or replace entity
```

**Zone Communication Issues:**
```python
# Validate zone compatibility
validation = validate_task_for_zone(task_id, zone)
if not validation['allowed']:
    print(f"Task blocked in zone {zone}: {validation['reason']}")
    # Move to appropriate zone
```

## Advanced Features

### Custom Task Types

```python
# Define custom task types
register_task_type("code_review", {
    "description": "Automated code review process",
    "required_capabilities": ["code_analysis", "security_review"],
    "estimated_duration": 15  # minutes
})
```

### Task Templates

```python
# Create task templates for common workflows
create_task_template("security_audit", {
    "description": "Comprehensive security audit",
    "chain": ["security_scanner", "vulnerability_analyzer", "report_generator"],
    "zone": "trusted_ai"
})
```

### Dynamic Task Adjustment

```python
# Adjust task parameters during execution
adjust_task(task_id, {
    "priority": "critical",
    "deadline": datetime.now() + timedelta(minutes=10)
})
```

## Future Enhancements

### Planned Features

1. **Multi-Modal Tasks**: Support for text, image, audio task processing
2. **Federated Task Execution**: Tasks spanning multiple organizations
3. **Real-time Collaboration**: Live task editing and coordination
4. **Task Marketplaces**: Public task delegation and execution
5. **AI Task Learning**: Automatic task optimization based on performance

### Research Areas

1. **Task Decomposition**: Automatic breaking of complex tasks
2. **Quality Assurance**: Automated task result validation
3. **Resource Optimization**: Optimal resource allocation for tasks
4. **Fault Tolerance**: Robust handling of entity failures
5. **Performance Prediction**: Estimating task completion times

---

## Summary

The Dynamic AI Playbook system provides sophisticated task delegation and coordination capabilities, enabling AI entities to coordinate complex workflows with proper context preservation, zone awareness, and session management. With support for Docker container AI spawning, IPFS zone synchronization, and comprehensive monitoring, it creates a robust foundation for distributed AI coordination.

**Key Capabilities:**
- ✅ Sophisticated task delegation with AI-to-AI instruction crafting
- ✅ Context-aware communication with zone-based routing
- ✅ Docker container AI management with session-based coordination
- ✅ Comprehensive monitoring and debugging capabilities
- ✅ Production-ready task lifecycle management

**Ready for complex distributed AI workflows and coordination scenarios!** 🤖💬🔗📊🎯🚀