Developer of brain-inspired AI systems specializing in prefrontal cortex simulation and cognitive augmentation.

Overview

SCANUE v22 employs a sophisticated multi-agent architecture where different agents specialize in specific cognitive functions. This design enables efficient task distribution and leverages the strengths of each agent type.

Base Agent

The foundation of all agents in the system, providing core functionality and common interfaces.

Features

  • Common Interface: Standardized methods for agent communication
  • State Management: Persistent state handling across interactions
  • Error Handling: Robust error management and recovery
  • Logging: Comprehensive logging for debugging and monitoring

Usage

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from agents.base import BaseAgent

agent = BaseAgent(name="base_agent")
result = agent.process(input_data)

DLPFC Agent

The Dorsolateral Prefrontal Cortex (DLPFC) agent specializes in executive functions and cognitive control, mimicking the cognitive processes of the human prefrontal cortex.

Capabilities

  • Executive Control: High-level decision making and task coordination
  • Working Memory: Temporary information storage and manipulation
  • Cognitive Flexibility: Adapting strategies based on context
  • Attention Management: Focusing on relevant information

Specialized Functions

  • Strategic planning and goal management
  • Conflict monitoring and resolution
  • Abstract reasoning and problem-solving
  • Multi-tasking coordination

Usage

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from agents.dlpfc import DLPFCAgent

dlpfc_agent = DLPFCAgent(name="executive_agent")
strategic_plan = dlpfc_agent.create_strategy(problem_context)
decision = dlpfc_agent.make_executive_decision(options)

Specialized Agents

Task-specific agents optimized for particular domains or functions.

Design Principles

  • Domain Expertise: Deep specialization in specific areas
  • Efficient Processing: Optimized algorithms for target tasks
  • Interoperability: Seamless integration with other agents
  • Scalability: Ability to handle varying workloads

Implementation Example

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from agents.specialized import SpecializedAgent

class AnalysisAgent(SpecializedAgent):
    def __init__(self):
        super().__init__(name="analysis_agent")
        self.specialty = "data_analysis"
    
    def analyze_data(self, dataset):
        # Specialized analysis logic
        return self.perform_analysis(dataset)

Agent Coordination

Communication Patterns

  • Direct Messaging: Point-to-point communication between agents
  • Broadcast Messages: One-to-many communication for coordination
  • Event-Driven Updates: Reactive communication based on system events

Workflow Integration

Agents are seamlessly integrated into the LangGraph workflow engine, allowing for:

  • Dynamic agent selection based on task requirements
  • Parallel agent execution for improved performance
  • Sequential agent chaining for complex processing pipelines

Example Coordination

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from workflow import SCANUEWorkflow
from agents.dlpfc import DLPFCAgent
from agents.specialized import SpecializedAgent

workflow = SCANUEWorkflow()

# Add coordinated agents
executive_agent = DLPFCAgent(name="executive")
analysis_agent = SpecializedAgent(name="analyzer")

workflow.add_agent_chain([executive_agent, analysis_agent])

Human-Agent Interaction

Interactive Decision Points

Agents can request human input at critical decision points, ensuring that human expertise is leveraged when needed.

Feedback Learning

Agents adapt their behavior based on human feedback, improving performance over time.

Collaboration Modes

  • Advisory Mode: Agents provide recommendations for human decision-making
  • Supervisory Mode: Humans oversee agent actions with intervention capabilities
  • Collaborative Mode: Real-time collaboration between humans and agents

Performance Monitoring

Metrics Collection

  • Response Time: Agent processing duration
  • Accuracy: Task completion success rates
  • Resource Usage: Memory and CPU utilization
  • Interaction Quality: Human feedback scores

Optimization Strategies

  • Load Balancing: Distributing tasks across available agents
  • Caching: Storing frequently accessed data for faster retrieval
  • Model Fine-tuning: Adjusting agent parameters based on performance data

Testing and Validation

Each agent type includes comprehensive test coverage:

  • Unit Tests: Individual agent functionality
  • Integration Tests: Agent interaction and coordination
  • Performance Tests: Load and stress testing
  • Behavioral Tests: Validation of cognitive functions

Learn more about implementing custom agents in our development documentation.