📎4. AI Agent Coordination System

4.1 Multi-Agent Framework

DeLLM's multi-agent system is designed for optimal collaboration and efficiency:

4.1.1 Agent Communication Protocol

Agents communicate through a structured messaging system:

json
{  
    "message_type": "task_request",  
    "from_agent": "coordinator",  
    "to_agent": "investment_analyzer",  
    "task_id": "INV_001",  
    "parameters": {    
        "token_address": "0x...",    
        "analysis_depth": "comprehensive",    
        "timeframe": "30d"  },  
    "dependencies": ["market_data", "contract_analysis"],  
    "deadline": "2025-06-30T15:00:00Z"
}

4.1.2 Task Scheduling and Prioritization

• Priority Queuing: High-impact tasks receive processing priority

• Resource Allocation: Dynamic allocation based on agent availability

• Deadline Management: Ensuring time-sensitive analyses complete on schedule

• Load Balancing: Distributing workload across available agents

4.1.3 Quality Assurance Framework

• Cross-Validation: Multiple agents verify critical analyses

• Confidence Scoring: Each analysis includes confidence metrics

• Error Detection: Automated identification of inconsistencies

• Feedback Loops: Continuous improvement based on outcome tracking

4.2 Autonomous Decision Making

4.2.1 Decision Trees and Logic

DeLLM employs sophisticated decision-making frameworks:

• Risk-Reward Analysis: Automated evaluation of potential actions

• Scenario Planning: Multiple outcome modeling and preparation

• Threshold Management: Automated triggers for specific actions

• Escalation Protocols: When to seek human input or approval

4.2.2 Learning and Adaptation

• Performance Tracking: Monitoring the success of recommendations and strategies

• Pattern Recognition: Identifying successful approaches for future use

• Model Updates: Continuous improvement of analytical models

• Strategy Refinement: Optimization based on market feedback