Agent and Tools

Dotbase provides a comprehensive suite of agent types and tools for building sophisticated AI workforces. Built on Microsoft's Autogen framework, our components enable seamless integration and collaboration between different AI agents.

Agent Types

Nexus

A configurable ConversableAgent subclass designed for specialized tasks and workflows. This agent type serves as the foundation for creating specialized AI workers with defined roles and capabilities.

interface AgentConfig {
    name: string;
    systemPrompt: string;
    model: string;
    temperature?: number;
    maxTokens?: number;
    tools?: Function[];
}

Key Features

• Dynamic system prompt configuration for role definition

• Custom tool integration for enhanced capabilities

• Adjustable model parameters for response control

• Memory management for conversation context

• Task specialization through prompt engineering

• Real-time learning and adaptation

• Multi-step reasoning capabilities

• Error recovery and fallback strategies

Use Cases

• Data analysis and processing

• Content generation and editing

• Research assistance

• Code generation and review

• Technical documentation

• Problem-solving tasks

Lumina

Integration with NovaMind Assistant API for enhanced capabilities. This agent type leverages NovaMind specialized assistants with their unique capabilities and knowledge bases.

interface NovaMindAgentConfig {
    assistantId: string;
    name: string;
    tools: string[];
    model?: string;
    metadata?: Record<string, unknown>;
}

Capabilities

• NovaMind Assistant integration with custom configurations

• Specialized function support for complex tasks

• Advanced knowledge retrieval using NovaMind systems

• Code interpretation and execution

• File handling and analysis

• Multi-modal input processing

• Dynamic context management

• Integration with external tools and APIs

Applications

• Complex data analysis

• Natural language processing

• Code generation and debugging

• Document analysis and summary

• Mathematical computations

• API integration tasks

Bridge

Interface between human operators and AI agents. This component manages all human-AI interactions and feedback loops.

interface InterfaceConfig {
    name: string;
    humanFeedback: boolean;
    maxRetries?: number;
    feedbackType?: 'immediate' | 'batched';
}

Features

• Real-time feedback handling for continuous improvement

• Task delegation with priority management

• Result verification and quality assurance

• Error management and recovery

• Input validation and preprocessing

• Output formatting and presentation

• Session management and context preservation

• User preference handling

Interaction Modes

• Synchronous communication

• Asynchronous batch processing

• Interactive debugging

• Multi-step validation

• Progress monitoring

Synergy Hub

Orchestration hub for multi-agent collaboration. This central component manages agent interactions and workflow coordination.

interface HubConfig {
    agents: string[];
    maxRounds: number;
    enableMemory: boolean;
    timeoutSeconds?: number;
    adminAgent?: string;
}

Core Functions

• Agent communication routing

• Task distribution and load balancing

• Conversation flow management

• Memory synchronization

• Error handling and recovery

• Performance monitoring

• Resource allocation

• State management

Advanced Features

• Dynamic agent allocation

• Priority-based scheduling

• Conflict resolution

• Conversation checkpointing

• Performance optimization

• Security enforcement

Tools

Spark

Extensible function integration for enhanced agent capabilities. Functions serve as modular tools that agents can use to perform specific tasks.

def function(
    param1: str,
    param2: List[str],
    **kwargs
) -> Dict[str, Any]:
    """
    Function template for custom tool integration.
    
    Args:
        param1: Primary parameter
        param2: List of secondary parameters
        **kwargs: Additional parameters
        
    Returns:
        Dict containing operation results
    """
    results = {}
    # Implementation
    return results

Features

• Custom Python function integration

• NovaMind function calling support

• Error handling and validation

• Input/output type safety

• Performance monitoring

• Resource management

• Asynchronous execution

• Retry mechanisms

Implementation Examples

Data Analysis Pipeline

sequenceDiagram
    participant I as Interface
    participant H as Hub
    participant A as Agent
    participant F as Function
    
    I->>H: Analysis Request
    H->>A: Delegate Task
    A->>F: Process Data
    F-->>A: Results
    A->>H: Analysis
    H->>I: Final Report

Configuration Best Practices

# Agent Configuration
agent_config = {
    "system_prompt": """
    Role: Data Analysis Specialist
    Objective: Process and analyze complex datasets
    Constraints:
    - Validate input data
    - Handle missing values
    - Provide statistical significance
    """,
    "model": "gpt-4",
    "temperature": 0.7,
    "tools": ["data_processor", "statistical_analyzer"]
}

# Error Handling
error_config = {
    "max_retries": 3,
    "retry_delay": "exponential",
    "fallback_strategy": "default_response",
    "logging": "verbose"
}

Security & Performance

Feature	Implementation
Authentication	OAuth 2.0 / JWT
Rate Limiting	Token bucket algorithm
Monitoring	Prometheus metrics
Logging	Structured JSON logs
Caching	Redis with LRU policy