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SwarmAgentic-Inspired Experiments for QuestMaster

Overview​

This document outlines a series of experiments inspired by SwarmAgentic and other cutting-edge multi-agent frameworks to enhance QuestMaster's capabilities. These experiments focus on autonomous agent generation, swarm optimization, and collaborative intelligence.

🎯 Experiment Categories​

1. Initialization & Agent Generation​

  • LLM-based team initialization from scratch
  • Modular role definition for tools & memory

2. Swarm Optimization Loop & Refinement​

  • Symbolic PSO (Particle Swarm Optimization) task planning
  • Failure-aware flaw identification & velocity updates
  • Tool use and workflow sequence optimization

3. Collaboration Structure & Adaptive Feedback​

  • Inter-agent feedback and observer roles
  • Real-time workflow reconfiguration

4. Evaluation & Benchmarking​

  • Objective function & fitness evaluation setup
  • Benchmark comparison and ablation testing

🧪 Detailed Experiments​

Experiment 1: LLM-Based Team Initialization from Scratch​

Objective: Remove QuestMaster's dependence on predefined agent templates by generating an initial team of agents purely from the task description.

Required Components:

  • Access to LLM APIs (for role synthesis)
  • QuestMaster's memory store (to record generated roles/policies)

Implementation Sketch:

// Prompt LLM to generate agent team from task description
const generateAgentTeam = async (taskDescription: string) => {
  // Use temperature-controlled sampling for diversity
  const lowTempTeam = await generateWithTemp(0.3);  // Safe designs
  const highTempTeam = await generateWithTemp(0.9); // Creative designs
  
  // Each agent includes:
  return {
    role: { identifier: string, responsibility: string },
    policy: { toolUsage: string[], decisionRules: string[] }
  };
};

Expected Outcome: A pool of varied initial agentic systems instantiated "from scratch," addressing the limitation of prior frameworks that required hard-coded role templates.

Experiment 2: Modular Role Definition for Tools & Memory​

Objective: Structure each generated agent into clear modules (planning, reasoning, tool-use, memory) to facilitate systematic optimization.

Required Components:

  • QuestMaster's agent class definitions (to support modular attributes)
  • LLM prompt templates for module-specific generation

Implementation Sketch:

interface ModularAgent {
  planning: {
    method: string;        // How the agent breaks down tasks
    strategies: string[];
  };
  toolUse: {
    availableTools: MCPTool[];
    selectionStrategy: string;
  };
  memory: {
    utilizationPlan: string;
    retentionPolicy: string;
  };
  reasoning: {
    approach: string;
    constraints: string[];
  };
}

Expected Outcome: Agents with well-defined sub-modules for tool calls and memory, making it easier to apply targeted refinements.

Experiment 3: Symbolic PSO Task Planning Loop​

Objective: Integrate a Particle Swarm Optimization loop that evolves QuestMaster's task plans and agent configurations over multiple iterations.

Required Components:

  • Fitness evaluation function
  • Population manager (track personal/global best solutions)
  • LLM APIs for generating plan transformations
  • Integration with MCP orchestration

Implementation Sketch:

class SymbolicPSO {
  private particles: AgentTeamConfig[];
  private globalBest: AgentTeamConfig;
  private personalBests: Map<string, AgentTeamConfig>;
  
  async iterate() {
    for (const particle of this.particles) {
      // Combine three influences:
      // 1. Personal best configuration
      // 2. Global best configuration  
      // 3. Directed fixes from flaw analysis
      
      const velocity = await this.llm.generateModifications({
        current: particle,
        personalBest: this.personalBests.get(particle.id),
        globalBest: this.globalBest,
        flawAnalysis: await this.analyzeFlaws(particle)
      });
      
      // Apply modifications
      particle.apply(velocity);
      
      // Re-evaluate fitness
      const fitness = await this.evaluate(particle);
      this.updateBests(particle, fitness);
    }
  }
}

Expected Outcome: An automated planner that jointly optimizes agent functionalities and their collaboration strategy over iterations.

Experiment 4: Failure-Aware Flaw Identification​

Objective: Implement a refinement mechanism that identifies specific flaws in a plan's execution and updates the plan in a failure-aware manner.

Required Components:

  • LLM prompt for flaw analysis
  • Data storage for tracking past flaws and attempted fixes
  • Logic to incorporate personal/global best references

Implementation Sketch:

interface FlawAnalysis {
  agentFlaws: Array<{
    agent: string;
    issue: string;
    severity: number;
  }>;
  collaborationFlaws: Array<{
    description: string;
    affectedAgents: string[];
  }>;
  suggestedFixes: Array<{
    target: string;
    modification: string;
    rationale: string;
  }>;
}

async function analyzeAndFix(execution: ExecutionResult) {
  // Diagnose issues
  const flaws = await llm.analyzeFlaws(execution);
  
  // Generate failure-aware updates
  const updates = await llm.generateFailureAwareUpdates({
    flaws,
    previousAttempts: this.failureHistory.get(flaws),
    globalBest: this.globalBestSolution
  });
  
  // Track what we've tried
  this.failureHistory.set(flaws, updates);
  
  return updates;
}

Expected Outcome: QuestMaster's plans improve in a targeted fashion with each iteration, systematically eliminating recurring flaws.

Experiment 5: Tool Use and Workflow Optimization​

Objective: Apply swarm optimization specifically to how tools are orchestrated and tasks sequenced in QuestMaster's plans.

Required Components:

  • Workflow representation (easily editable)
  • Fitness metrics for workflow efficiency
  • LLM prompts for reordering/modifying steps

Implementation Sketch:

interface WorkflowOptimization {
  operations: {
    reorder: (step1: number, step2: number) => void;
    merge: (steps: number[]) => void;
    split: (step: number) => void;
    reassignTool: (step: number, newTool: string) => void;
    insertStep: (position: number, step: WorkflowStep) => void;
    removeStep: (step: number) => void;
  };
  
  async optimize(workflow: Workflow) {
    // Analyze current workflow
    const analysis = await this.analyzeEfficiency(workflow);
    
    // Generate transformations
    const transformations = await this.llm.suggestOptimizations({
      workflow,
      bottlenecks: analysis.bottlenecks,
      redundancies: analysis.redundancies,
      toolUsagePatterns: analysis.toolPatterns
    });
    
    // Apply and evaluate
    return this.applyTransformations(workflow, transformations);
  }
}

Expected Outcome: More efficient and reliable task sequences with optimized tool utilization.

Experiment 6: Inter-Agent Feedback and Observer Role​

Objective: Enhance the QuestMaster agent team with an internal feedback loop by introducing an observer or reviewer mechanism.

Required Components:

  • New agent role (Observer/Critic)
  • Integration of feedback into workflow
  • LLM prompts for analysis and critique

Implementation Sketch:

class ObserverAgent {
  role = "Observer";
  
  async reviewExecution(
    agents: Agent[],
    results: ExecutionResult[]
  ): Promise<Feedback> {
    // Analyze outputs without producing task output
    const critique = await this.llm.analyze({
      prompt: "Critique the solution and identify errors or improvements",
      context: { agents, results }
    });
    
    // Provide actionable feedback
    return {
      overallAssessment: critique.assessment,
      agentSpecificFeedback: critique.perAgentFeedback,
      suggestedRevisions: critique.revisions,
      confidenceScore: critique.confidence
    };
  }
  
  async performSanityCheck(answer: any, criteria: Criteria) {
    // Cross-verify against known criteria or test cases
    return this.validate(answer, criteria);
  }
}

Expected Outcome: Increased solution accuracy and robustness through internal quality control and self-reflection capabilities.

Experiment 7: Real-Time Workflow Reconfiguration​

Objective: Enable QuestMaster to reconfigure its plan on the fly in response to failures or new information.

Required Components:

  • Execution monitoring hooks
  • Dynamic workflow modification mechanism
  • LLM support for quick re-planning

Implementation Sketch:

class DynamicWorkflowManager {
  async handleExecutionEvent(event: ExecutionEvent) {
    if (event.type === 'ERROR' || event.type === 'UNEXPECTED_RESULT') {
      // Pause execution
      this.pauseWorkflow();
      
      // Generate modifications mid-execution
      const modifications = await this.llm.replan({
        currentState: this.getWorkflowState(),
        error: event.error,
        remainingSteps: this.getRemainingSteps(),
        observerFeedback: await this.observer.quickAnalysis()
      });
      
      // Options could include:
      // - Retry with different tool
      // - Insert recovery step
      // - Reassign to different agent
      // - Skip and compensate later
      
      this.applyModifications(modifications);
      this.resumeWorkflow();
    }
  }
}

Expected Outcome: Higher completion rate on complex tasks with unpredictable steps, as the system continuously adapts rather than failing.

Experiment 8: Objective Function & Fitness Evaluation​

Objective: Define clear metrics to quantitatively evaluate each agentic system's performance on a quest.

Required Components:

  • Task-specific evaluation scripts
  • Integration with QuestMaster's run pipeline
  • Score logging and comparison

Implementation Sketch:

interface FitnessFunction {
  evaluate(execution: QuestExecution): Promise<FitnessScore>;
}

class CompositeFitness implements FitnessFunction {
  components = [
    { name: 'completeness', weight: 0.3, evaluator: new CompletenessEvaluator() },
    { name: 'efficiency', weight: 0.2, evaluator: new EfficiencyEvaluator() },
    { name: 'accuracy', weight: 0.3, evaluator: new AccuracyEvaluator() },
    { name: 'resourceUsage', weight: 0.2, evaluator: new ResourceEvaluator() }
  ];
  
  async evaluate(execution: QuestExecution): Promise<FitnessScore> {
    const scores = await Promise.all(
      this.components.map(c => c.evaluator.evaluate(execution))
    );
    
    return {
      total: this.weightedSum(scores),
      breakdown: scores,
      metadata: execution.metadata
    };
  }
}

Expected Outcome: Reliable quantitative measurement of progress, enabling data-driven optimization decisions.

Experiment 9: Benchmark Comparison and Ablation Testing​

Objective: Rigorously evaluate the upgraded QuestMaster against baseline approaches and perform ablations to validate each component's contribution.

Required Components:

  • Baseline agent system
  • Suite of test tasks
  • Logging/analysis tools

Implementation Sketch:

class BenchmarkSuite {
  baselines = {
    original: new OriginalQuestMaster(),
    withPSO: new QuestMasterWithPSO(),
    withObserver: new QuestMasterWithObserver(),
    full: new QuestMasterFullSwarm()
  };
  
  testTasks = [
    new TravelPlannerTask(),
    new CreativeReasoningTask(),
    new CodeGenerationTask(),
    new ResearchTask()
  ];
  
  async runComparison() {
    const results = new Map();
    
    for (const [name, system] of Object.entries(this.baselines)) {
      for (const task of this.testTasks) {
        const metrics = await this.evaluate(system, task);
        results.set(`${name}-${task.name}`, metrics);
      }
    }
    
    return this.analyzeResults(results);
  }
  
  async runAblation() {
    const features = ['PSO', 'FlawAnalysis', 'Observer', 'DynamicReconfig'];
    const ablationResults = [];
    
    for (const feature of features) {
      const systemWithout = this.createSystemWithoutFeature(feature);
      const performance = await this.evaluate(systemWithout);
      ablationResults.push({ 
        disabled: feature, 
        performance,
        impact: this.calculateImpact(performance)
      });
    }
    
    return ablationResults;
  }
}

Expected Outcome: Detailed performance report showing improvements and validating the necessity of each component.

📊 Success Metrics​

Quantitative Metrics​

  • Success Rate Improvement: Target >250% over baseline (SwarmAgentic achieved 261.8%)
  • Convergence Speed: Iterations to reach stable high performance
  • Resource Efficiency: Compute cost per successful quest
  • Robustness: Performance on edge cases and failure recovery

Qualitative Metrics​

  • Agent Diversity: Variety in generated agent teams
  • Solution Creativity: Novel approaches discovered by the system
  • Adaptability: How well system handles unexpected scenarios
  • Interpretability: Clarity of agent decisions and workflows

🚀 Implementation Roadmap​

Phase 1: Foundation (Weeks 1-2)​

  • Implement basic PSO framework
  • Create modular agent architecture
  • Set up fitness evaluation system

Phase 2: Core Features (Weeks 3-4)​

  • Develop flaw analysis system
  • Implement observer agent
  • Create workflow optimization

Phase 3: Advanced Features (Weeks 5-6)​

  • Real-time reconfiguration
  • Multi-agent feedback loops
  • Advanced tool orchestration

Phase 4: Evaluation (Weeks 7-8)​

  • Comprehensive benchmarking
  • Ablation studies
  • Performance tuning

🔮 Future Directions​

Advanced Swarm Techniques​

  • Ant Colony Optimization: For path-finding in solution space
  • Genetic Algorithms: For agent evolution across generations
  • Reinforcement Learning: For long-term strategy optimization

Multi-Swarm Coordination​

  • Multiple specialized swarms for different task types
  • Inter-swarm communication and knowledge transfer
  • Hierarchical swarm structures

Emergent Behaviors​

  • Self-organizing agent teams
  • Spontaneous role specialization
  • Collective problem-solving strategies

📚 References​

  • SwarmAgentic: Language Agents as Swarm Intelligence
  • AgentSquare: Modular Agent Design Framework
  • ADAS: Automated Design of Agentic Systems
  • AutoAgents: Automatic Agent Generation Framework

This document represents experimental features that push the boundaries of autonomous agent systems. Implementation should be approached iteratively with careful evaluation at each stage.