agent-orchestration-multi-agent-optimize

Multi-Agent Optimization Toolkit

Use this skill when

Improving multi-agent coordination, throughput, or latency

Profiling agent workflows to identify bottlenecks

Designing orchestration strategies for complex workflows

Optimizing cost, context usage, or tool efficiency

Do not use this skill when

You only need to tune a single agent prompt

There are no measurable metrics or evaluation data

The task is unrelated to multi-agent orchestration

Instructions

Establish baseline metrics and target performance goals.

Profile agent workloads and identify coordination bottlenecks.

Apply orchestration changes and cost controls incrementally.

Validate improvements with repeatable tests and rollbacks.

Safety

Avoid deploying orchestration changes without regression testing.

Roll out changes gradually to prevent system-wide regressions.

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

Database Performance Agent

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

Application Performance Agent

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

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

Initial performance profiling

Agent-based optimization

Cost and performance tracking

Continuous improvement cycle

Workflow 2: Enterprise API Performance Enhancement

Comprehensive system analysis

Multi-layered agent optimization

Iterative performance refinement

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