7. Multi-Agent Task Execution
7.1 Task Decomposition Framework
7.1.1 Complex Task Breakdown
When users request comprehensive analysis, DeLLM automatically decomposes tasks:
Example: "Analyze XYZ token for investment potential"
Initial Assessment (Market Agent)
Current price and market cap
Trading volume and liquidity
Exchange listings and availability
Technical Analysis (Investment Agent)
Tokenomics structure and distribution
Smart contract security analysis
Utility and use case evaluation
Risk Assessment (Risk Agent)
Security vulnerabilities
Regulatory compliance
Market and liquidity risks
Competitive Analysis (Market Agent)
Comparable projects and positioning
Market opportunity and competition
Differentiation factors
Final Synthesis (Coordination Engine)
Comprehensive investment recommendation
Risk-reward assessment
Action plan and next steps
7.1.2 Dynamic Task Routing
Tasks are routed based on:
Agent Expertise: Matching tasks to specialized capabilities
Current Workload: Balancing load across available agents
Data Dependencies: Ensuring prerequisite information is available
Urgency Level: Prioritizing time-sensitive analyses
Quality Requirements: Assigning complex tasks to highest-capability agents
7.2 Result Integration and Synthesis
7.2.1 Multi-Agent Output Combination
DeLLM synthesizes results from multiple agents:
Conflict Resolution: Handling disagreements between agent analyses
Confidence Weighting: Emphasizing higher-confidence assessments
Completeness Checking: Ensuring all required analysis components are present
Quality Validation: Cross-checking results for consistency and accuracy
7.2.2 Report Generation
Automated creation of comprehensive reports:
Executive Summary: Key findings and recommendations
Detailed Analysis: In-depth technical and fundamental analysis
Risk Assessment: Comprehensive risk evaluation and mitigation strategies
Action Items: Specific next steps and recommendations
Supporting Data: Charts, tables, and detailed calculations
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