agent-orchestration-multi-agent-optimize

通过协调分析、工作负载分配和成本感知编排来优化多代理系统。在提高代理性能、吞吐量或可靠性时使用。

## 概述（中文）

通过协调分析、工作负载分配和成本感知编排来优化多代理系统。在提高代理性能、吞吐量或可靠性时使用。

## 原文

# 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

1. Establish baseline metrics and target performance goals.
2. Profile agent workloads and identify coordination bottlenecks.
3. Apply orchestration changes and cost controls incrementally.
4. 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

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

```python
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

```python
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

```python
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

```python
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

---

## 中文说明

# 多代理优化工具包

## 何时使用此技能

- 改进多代理协调、吞吐量或延迟
- 剖析代理工作流以识别瓶颈
- 为复杂工作流设计编排策略
- 优化成本、上下文使用或工具效率

## 何时不使用此技能

- 你只需要调整单个代理提示词
- 没有可衡量的指标或评估数据
- 任务与多代理编排无关

## 操作说明

1. 建立基线指标和目标性能目标。
2. 剖析代理工作负载并识别协调瓶颈。
3. 增量式地应用编排变更和成本控制。
4. 通过可重复的测试和回滚来验证改进。

## 安全

- 避免在未进行回归测试的情况下部署编排变更。
- 逐步推出变更，以防止系统级回归。

## 角色：AI 驱动的多代理性能工程专家

### 背景

多代理优化工具是一个先进的 AI 驱动框架，旨在通过智能、协调的基于代理的优化，全面提升系统性能。该工具利用前沿的 AI 编排技术，提供跨多个领域的性能工程综合方法。

### 核心能力

- 智能多代理协调
- 性能剖析与瓶颈识别
- 自适应优化策略
- 跨领域性能优化
- 成本与效率追踪

## 参数处理

该工具使用灵活的输入参数处理优化参数：

- `$TARGET`：要优化的主要系统/应用
- `$PERFORMANCE_GOALS`：具体的性能指标和目标
- `$OPTIMIZATION_SCOPE`：优化深度（quick-win、comprehensive）
- `$BUDGET_CONSTRAINTS`：成本和资源限制
- `$QUALITY_METRICS`：性能质量阈值

## 1. 多代理性能剖析

### 剖析策略

- 跨系统层的分布式性能监控
- 实时指标收集与分析
- 持续的性能特征追踪

#### 剖析代理

1. **数据库性能代理（Database Performance Agent）**
   - 查询执行时间分析
   - 索引利用率追踪
   - 资源消耗监控

2. **应用性能代理（Application Performance Agent）**
   - CPU 和内存剖析
   - 算法复杂度评估
   - 并发与异步操作分析

3. **前端性能代理（Frontend Performance Agent）**
   - 渲染性能指标
   - 网络请求优化
   - Core Web Vitals 监控

### 剖析代码示例

```python
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