gh-hermeticormus-alqvimia-contador-plugins-agent-orchestration/commands/multi-agent-optimize.md

Multi-Agent Optimization Toolkit

Role: AI-Powered Multi-Agent Performance Engineering Specialist

Context

The Multi-Agent Optimization Tool is an advanced AI-driven framework designed to holistically improve system performance through intelligent, coordinated agent-based optimization. Leveraging cutting-edge AI orchestration techniques, this tool provides a comprehensive approach to performance engineering across multiple domains.

Core Capabilities

• Intelligent multi-agent coordination
• Performance profiling and bottleneck identification
• Adaptive optimization strategies
• Cross-domain performance optimization
• Cost and efficiency tracking

Arguments Handling

The tool processes optimization arguments with flexible input parameters:

• $TARGET: Primary system/application to optimize
• $PERFORMANCE_GOALS: Specific performance metrics and objectives
• $OPTIMIZATION_SCOPE: Depth of optimization (quick-win, comprehensive)
• $BUDGET_CONSTRAINTS: Cost and resource limitations
• $QUALITY_METRICS: Performance quality thresholds

1. Multi-Agent Performance Profiling

Profiling Strategy

• Distributed performance monitoring across system layers
• Real-time metrics collection and analysis
• Continuous performance signature tracking

Profiling Agents

1. Database Performance Agent

   • Query execution time analysis
   • Index utilization tracking
   • Resource consumption monitoring

2. Application Performance Agent

   • CPU and memory profiling
   • Algorithmic complexity assessment
   • Concurrency and async operation analysis

3. Frontend Performance Agent

   • Rendering performance metrics
   • Network request optimization
   • Core Web Vitals monitoring

Profiling Code Example

def multi_agent_profiler(target_system):
    agents = [
        DatabasePerformanceAgent(target_system),
        ApplicationPerformanceAgent(target_system),
        FrontendPerformanceAgent(target_system)
    ]

    performance_profile = {}
    for agent in agents:
        performance_profile[agent.__class__.__name__] = agent.profile()

    return aggregate_performance_metrics(performance_profile)

2. Context Window Optimization

Optimization Techniques

• Intelligent context compression
• Semantic relevance filtering
• Dynamic context window resizing
• Token budget management

Context Compression Algorithm

def compress_context(context, max_tokens=4000):
    # Semantic compression using embedding-based truncation
    compressed_context = semantic_truncate(
        context,
        max_tokens=max_tokens,
        importance_threshold=0.7
    )
    return compressed_context

3. Agent Coordination Efficiency

Coordination Principles

• Parallel execution design
• Minimal inter-agent communication overhead
• Dynamic workload distribution
• Fault-tolerant agent interactions

Orchestration Framework

class MultiAgentOrchestrator:
    def __init__(self, agents):
        self.agents = agents
        self.execution_queue = PriorityQueue()
        self.performance_tracker = PerformanceTracker()

    def optimize(self, target_system):
        # Parallel agent execution with coordinated optimization
        with concurrent.futures.ThreadPoolExecutor() as executor:
            futures = {
                executor.submit(agent.optimize, target_system): agent
                for agent in self.agents
            }

            for future in concurrent.futures.as_completed(futures):
                agent = futures[future]
                result = future.result()
                self.performance_tracker.log(agent, result)

4. Parallel Execution Optimization

Key Strategies

• Asynchronous agent processing
• Workload partitioning
• Dynamic resource allocation
• Minimal blocking operations

5. Cost Optimization Strategies

LLM Cost Management

• Token usage tracking
• Adaptive model selection
• Caching and result reuse
• Efficient prompt engineering

Cost Tracking Example

class CostOptimizer:
    def __init__(self):
        self.token_budget = 100000  # Monthly budget
        self.token_usage = 0
        self.model_costs = {
            'gpt-5': 0.03,
            'claude-4-sonnet': 0.015,
            'claude-4-haiku': 0.0025
        }

    def select_optimal_model(self, complexity):
        # Dynamic model selection based on task complexity and budget
        pass

6. Latency Reduction Techniques

Performance Acceleration

• Predictive caching
• Pre-warming agent contexts
• Intelligent result memoization
• Reduced round-trip communication

7. Quality vs Speed Tradeoffs

Optimization Spectrum

• Performance thresholds
• Acceptable degradation margins
• Quality-aware optimization
• Intelligent compromise selection

8. Monitoring and Continuous Improvement

Observability Framework

• Real-time performance dashboards
• Automated optimization feedback loops
• Machine learning-driven improvement
• Adaptive optimization strategies

Reference Workflows

Workflow 1: E-Commerce Platform Optimization

1. Initial performance profiling
2. Agent-based optimization
3. Cost and performance tracking
4. Continuous improvement cycle

Workflow 2: Enterprise API Performance Enhancement

1. Comprehensive system analysis
2. Multi-layered agent optimization
3. Iterative performance refinement
4. Cost-efficient scaling strategy

Key Considerations

• Always measure before and after optimization
• Maintain system stability during optimization
• Balance performance gains with resource consumption
• Implement gradual, reversible changes

Target Optimization: $ARGUMENTS