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Optimize multi-agent systems with coordinated profiling, workload distribution, and cost-aware orchestration. Use when improving agent performance, throughput, or reliability.
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| name | agent-orchestration-multi-agent-optimize |
| description | Optimize multi-agent systems with coordinated profiling, workload distribution, and cost-aware orchestration. Use when improving agent performance, throughput, or reliability. |
| type | skill |
| created | 2026-02-27T00:00:00.000Z |
| domain | ai-ml |
| category | llm-agents |
| risk | unknown |
| source | community |
| tags | ["skill","ai-ml","llm-agents","agent","orchestration","multi"] |
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.
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 thresholdsDatabase Performance Agent
Application Performance Agent
Frontend Performance Agent
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)
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
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)
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
Target Optimization: $ARGUMENTS