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