189 lines
5.5 KiB
Markdown
189 lines
5.5 KiB
Markdown
# 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
|
|
```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 |