Multi-Agent Cost Optimization Patterns

Overview

Multi-agent systems using Claude can quickly become expensive without proper optimization. This guide provides comprehensive patterns for reducing costs while maintaining performance across distributed agent architectures.

Cost Challenges in Multi-Agent Systems

• Redundant Context: Multiple agents processing the same background information
• Cascade Effects: One agent’s output becoming another’s input multiplies costs
• Resource Competition: Agents competing for API rate limits
• Context Explosion: Combined context from multiple agents exceeding limits
• Coordination Overhead: Meta-agents managing other agents add costs

Optimization Strategies

1. Shared Context Management

from typing import Dict, List, Optional, Set
import hashlib
import asyncio
from dataclasses import dataclass
from datetime import datetime, timedelta
 
@dataclass
class SharedContext:
    """Shared context that multiple agents can reference."""
    id: str
    content: str
    content_hash: str
    created_at: datetime
    last_accessed: datetime
    access_count: int
    agents_using: Set[str]
    is_cached: bool = False
    cache_tokens: int = 0
 
class MultiAgentContextManager:
    """Manages shared context across multiple agents to minimize redundancy."""
    
    def __init__(self, anthropic_client):
        self.client = anthropic_client
        self.shared_contexts: Dict[str, SharedContext] = {}
        self.agent_contexts: Dict[str, List[str]] = {}  # agent_id -> context_ids
        self.context_cache_status: Dict[str, bool] = {}
        
    async def register_shared_context(
        self, 
        content: str, 
        context_type: str = "general"
    ) -> str:
        """Register content that will be shared across agents."""
        content_hash = hashlib.sha256(content.encode()).hexdigest()
        
        # Check if this content already exists
        for ctx_id, ctx in self.shared_contexts.items():
            if ctx.content_hash == content_hash:
                ctx.last_accessed = datetime.utcnow()
                ctx.access_count += 1
                return ctx_id
        
        # Create new shared context
        context_id = f"{context_type}_{content_hash[:8]}"
        shared_context = SharedContext(
            id=context_id,
            content=content,
            content_hash=content_hash,
            created_at=datetime.utcnow(),
            last_accessed=datetime.utcnow(),
            access_count=1,
            agents_using=set(),
            cache_tokens=len(content) // 4  # Rough estimate
        )
        
        self.shared_contexts[context_id] = shared_context
        return context_id
    
    def assign_context_to_agent(self, agent_id: str, context_ids: List[str]):
        """Assign shared contexts to an agent."""
        if agent_id not in self.agent_contexts:
            self.agent_contexts[agent_id] = []
        
        for context_id in context_ids:
            if context_id in self.shared_contexts:
                self.agent_contexts[agent_id].append(context_id)
                self.shared_contexts[context_id].agents_using.add(agent_id)
    
    async def create_agent_message(
        self,
        agent_id: str,
        user_query: str,
        additional_context: Optional[str] = None,
        model: str = "claude-3-5-sonnet-20241022"
    ):
        """Create message with optimized context loading."""
        if agent_id not in self.agent_contexts:
            raise ValueError(f"Agent {agent_id} not registered")
        
        # Build message with shared contexts
        messages = []
        content_parts = []
        
        # Add shared contexts with caching
        for context_id in self.agent_contexts[agent_id]:
            if context_id in self.shared_contexts:
                context = self.shared_contexts[context_id]
                content_parts.append({
                    "type": "text",
                    "text": context.content,
                    "cache_control": {"type": "ephemeral"}
                })
                context.is_cached = True
        
        # Add agent-specific context if provided
        if additional_context:
            content_parts.append({
                "type": "text",
                "text": additional_context
            })
        
        # Add user query (not cached)
        content_parts.append({
            "type": "text",
            "text": user_query
        })
        
        messages = [{"role": "user", "content": content_parts}]
        
        # Execute request
        response = await self.client.messages.create(
            model=model,
            messages=messages,
            max_tokens=2000
        )
        
        return response
    
    def optimize_context_distribution(self) -> Dict[str, List[str]]:
        """Optimize how contexts are distributed among agents."""
        recommendations = {}
        
        # Find contexts used by multiple agents
        multi_agent_contexts = {
            ctx_id: ctx for ctx_id, ctx in self.shared_contexts.items()
            if len(ctx.agents_using) > 1
        }
        
        # Find contexts that should be merged
        similar_contexts = self._find_similar_contexts()
        
        # Generate recommendations
        for agent_id in self.agent_contexts:
            agent_recs = []
            
            # Recommend sharing contexts with other agents
            for ctx_id in self.agent_contexts[agent_id]:
                if ctx_id not in multi_agent_contexts:
                    similar_agents = self._find_agents_with_similar_needs(agent_id)
                    if similar_agents:
                        agent_recs.append(
                            f"Share context {ctx_id} with agents: {similar_agents}"
                        )
            
            recommendations[agent_id] = agent_recs
        
        return recommendations
    
    def _find_similar_contexts(self) -> List[Tuple[str, str, float]]:
        """Find contexts that are similar and could be merged."""
        similar_pairs = []
        context_ids = list(self.shared_contexts.keys())
        
        for i in range(len(context_ids)):
            for j in range(i + 1, len(context_ids)):
                ctx1 = self.shared_contexts[context_ids[i]]
                ctx2 = self.shared_contexts[context_ids[j]]
                
                # Simple similarity check (in practice, use embeddings)
                if len(ctx1.content) > 100 and len(ctx2.content) > 100:
                    similarity = self._calculate_similarity(ctx1.content, ctx2.content)
                    if similarity > 0.8:
                        similar_pairs.append((context_ids[i], context_ids[j], similarity))
        
        return similar_pairs
    
    def _calculate_similarity(self, text1: str, text2: str) -> float:
        """Calculate similarity between two texts."""
        # Simplified - in practice use proper embedding similarity
        words1 = set(text1.lower().split())
        words2 = set(text2.lower().split())
        intersection = words1.intersection(words2)
        union = words1.union(words2)
        return len(intersection) / len(union) if union else 0
    
    def _find_agents_with_similar_needs(self, agent_id: str) -> List[str]:
        """Find agents that could benefit from shared contexts."""
        similar_agents = []
        agent_contexts = set(self.agent_contexts.get(agent_id, []))
        
        for other_agent_id, other_contexts in self.agent_contexts.items():
            if other_agent_id != agent_id:
                other_contexts_set = set(other_contexts)
                overlap = len(agent_contexts.intersection(other_contexts_set))
                if overlap > len(agent_contexts) * 0.5:
                    similar_agents.append(other_agent_id)
        
        return similar_agents
    
    def get_cost_analysis(self) -> Dict[str, any]:
        """Analyze cost savings from context sharing."""
        total_contexts = len(self.shared_contexts)
        total_cache_tokens = sum(ctx.cache_tokens for ctx in self.shared_contexts.values())
        
        # Calculate savings
        cached_contexts = [ctx for ctx in self.shared_contexts.values() if ctx.is_cached]
        total_cached_tokens = sum(ctx.cache_tokens * ctx.access_count for ctx in cached_contexts)
        
        # Cost calculations (Claude 3.5 Sonnet prices)
        standard_cost = (total_cached_tokens / 1_000_000) * 3.00
        cached_cost = (total_cached_tokens / 1_000_000) * 0.30
        savings = standard_cost - cached_cost
        
        return {
            "total_shared_contexts": total_contexts,
            "total_agents": len(self.agent_contexts),
            "avg_contexts_per_agent": sum(len(ctxs) for ctxs in self.agent_contexts.values()) / len(self.agent_contexts) if self.agent_contexts else 0,
            "cache_hit_rate": len(cached_contexts) / total_contexts if total_contexts > 0 else 0,
            "estimated_savings": f"${savings:.2f}",
            "cost_reduction_percent": ((savings / standard_cost) * 100) if standard_cost > 0 else 0
        }

2. Agent Request Orchestration

import { EventEmitter } from 'events';
import PQueue from 'p-queue';
 
interface AgentRequest {
  id: string;
  agentId: string;
  priority: number;
  estimatedTokens: number;
  estimatedCost: number;
  dependencies: string[];
  timeout: number;
  retryCount: number;
  execute: () => Promise<any>;
}
 
interface AgentQuota {
  agentId: string;
  tokensPerMinute: number;
  tokensUsed: number;
  costBudget: number;
  costUsed: number;
  resetTime: number;
}
 
class MultiAgentOrchestrator extends EventEmitter {
  private requestQueue: PQueue;
  private agentQuotas: Map<string, AgentQuota> = new Map();
  private pendingRequests: Map<string, AgentRequest> = new Map();
  private completedRequests: Map<string, any> = new Map();
  private requestDependencies: Map<string, Set<string>> = new Map();
  
  constructor(private concurrency: number = 5) {
    super();
    
    this.requestQueue = new PQueue({ 
      concurrency,
      interval: 1000,
      intervalCap: 10 // Max 10 requests per second
    });
    
    // Reset quotas every minute
    setInterval(() => this.resetQuotas(), 60000);
  }
  
  registerAgent(
    agentId: string,
    tokensPerMinute: number,
    hourlyBudget: number
  ) {
    this.agentQuotas.set(agentId, {
      agentId,
      tokensPerMinute,
      tokensUsed: 0,
      costBudget: hourlyBudget / 60, // Per minute budget
      costUsed: 0,
      resetTime: Date.now() + 60000
    });
  }
  
  async submitRequest(request: AgentRequest): Promise<string> {
    // Validate agent quota
    const quota = this.agentQuotas.get(request.agentId);
    if (!quota) {
      throw new Error(`Agent ${request.agentId} not registered`);
    }
    
    // Check if request fits within quota
    if (!this.canExecuteRequest(request, quota)) {
      this.emit('request_deferred', {
        requestId: request.id,
        reason: 'quota_exceeded',
        deferredUntil: quota.resetTime
      });
      
      // Defer request until next quota reset
      setTimeout(() => this.submitRequest(request), quota.resetTime - Date.now());
      return request.id;
    }
    
    // Add to pending
    this.pendingRequests.set(request.id, request);
    
    // Track dependencies
    if (request.dependencies.length > 0) {
      this.requestDependencies.set(request.id, new Set(request.dependencies));
    }
    
    // Queue for execution
    this.requestQueue.add(() => this.executeRequest(request), {
      priority: request.priority
    });
    
    return request.id;
  }
  
  private canExecuteRequest(request: AgentRequest, quota: AgentQuota): boolean {
    return (
      quota.tokensUsed + request.estimatedTokens <= quota.tokensPerMinute &&
      quota.costUsed + request.estimatedCost <= quota.costBudget
    );
  }
  
  private async executeRequest(request: AgentRequest) {
    try {
      // Wait for dependencies
      await this.waitForDependencies(request.id);
      
      // Check quota again before execution
      const quota = this.agentQuotas.get(request.agentId)!;
      if (!this.canExecuteRequest(request, quota)) {
        // Requeue with delay
        setTimeout(() => this.submitRequest(request), 5000);
        return;
      }
      
      // Execute with timeout
      const timeoutPromise = new Promise((_, reject) => 
        setTimeout(() => reject(new Error('Request timeout')), request.timeout)
      );
      
      const result = await Promise.race([
        request.execute(),
        timeoutPromise
      ]);
      
      // Update quota
      quota.tokensUsed += request.estimatedTokens;
      quota.costUsed += request.estimatedCost;
      
      // Mark as completed
      this.completedRequests.set(request.id, result);
      this.pendingRequests.delete(request.id);
      
      this.emit('request_completed', {
        requestId: request.id,
        agentId: request.agentId,
        result
      });
      
      // Notify dependent requests
      this.notifyDependents(request.id);
      
    } catch (error) {
      // Handle retry logic
      if (request.retryCount > 0) {
        request.retryCount--;
        setTimeout(() => this.submitRequest(request), 5000);
      } else {
        this.emit('request_failed', {
          requestId: request.id,
          agentId: request.agentId,
          error
        });
      }
    }
  }
  
  private async waitForDependencies(requestId: string) {
    const dependencies = this.requestDependencies.get(requestId);
    if (!dependencies || dependencies.size === 0) return;
    
    const waitPromises = Array.from(dependencies).map(depId => 
      this.waitForCompletion(depId)
    );
    
    await Promise.all(waitPromises);
  }
  
  private async waitForCompletion(requestId: string): Promise<any> {
    // If already completed, return immediately
    if (this.completedRequests.has(requestId)) {
      return this.completedRequests.get(requestId);
    }
    
    // Wait for completion event
    return new Promise((resolve) => {
      const handler = (event: any) => {
        if (event.requestId === requestId) {
          this.off('request_completed', handler);
          resolve(event.result);
        }
      };
      this.on('request_completed', handler);
    });
  }
  
  private notifyDependents(completedRequestId: string) {
    for (const [requestId, deps] of this.requestDependencies) {
      if (deps.has(completedRequestId)) {
        deps.delete(completedRequestId);
        if (deps.size === 0) {
          this.requestDependencies.delete(requestId);
        }
      }
    }
  }
  
  private resetQuotas() {
    const now = Date.now();
    
    for (const quota of this.agentQuotas.values()) {
      if (now >= quota.resetTime) {
        quota.tokensUsed = 0;
        quota.costUsed = 0;
        quota.resetTime = now + 60000;
      }
    }
  }
  
  getSystemStatus() {
    const agentStats = Array.from(this.agentQuotas.values()).map(quota => ({
      agentId: quota.agentId,
      tokensUsedPercent: (quota.tokensUsed / quota.tokensPerMinute) * 100,
      budgetUsedPercent: (quota.costUsed / quota.costBudget) * 100,
      timeUntilReset: Math.max(0, quota.resetTime - Date.now())
    }));
    
    return {
      pendingRequests: this.pendingRequests.size,
      completedRequests: this.completedRequests.size,
      queueSize: this.requestQueue.size,
      queuePending: this.requestQueue.pending,
      agentStats
    };
  }
}

3. Dynamic Model Selection

from enum import Enum
from typing import Dict, List, Optional, Tuple
import numpy as np
 
class TaskComplexity(Enum):
    SIMPLE = "simple"
    MODERATE = "moderate"
    COMPLEX = "complex"
    CRITICAL = "critical"
 
class ModelCapability:
    def __init__(self, name: str, cost_per_mtok: float, quality_score: float, 
                 speed_score: float, context_length: int):
        self.name = name
        self.cost_per_mtok = cost_per_mtok
        self.quality_score = quality_score
        self.speed_score = speed_score
        self.context_length = context_length
 
class DynamicModelSelector:
    """Dynamically selects optimal model based on task requirements and constraints."""
    
    def __init__(self):
        # Define available models with their characteristics
        self.models = {
            "claude-3-5-haiku": ModelCapability(
                "claude-3-5-haiku", 0.80, 0.7, 0.9, 200000
            ),
            "claude-3-5-sonnet": ModelCapability(
                "claude-3-5-sonnet", 3.00, 0.85, 0.7, 200000
            ),
            "claude-4-opus": ModelCapability(
                "claude-4-opus", 15.00, 0.95, 0.5, 200000
            )
        }
        
        # Task complexity mappings
        self.complexity_requirements = {
            TaskComplexity.SIMPLE: {"min_quality": 0.6, "max_cost": 2.0},
            TaskComplexity.MODERATE: {"min_quality": 0.75, "max_cost": 5.0},
            TaskComplexity.COMPLEX: {"min_quality": 0.85, "max_cost": 20.0},
            TaskComplexity.CRITICAL: {"min_quality": 0.9, "max_cost": 100.0}
        }
        
        # Performance history for adaptive selection
        self.performance_history: Dict[str, List[float]] = {
            model: [] for model in self.models
        }
    
    def select_model(
        self,
        task_description: str,
        context_length: int,
        complexity: Optional[TaskComplexity] = None,
        quality_threshold: Optional[float] = None,
        budget_constraint: Optional[float] = None,
        speed_priority: float = 0.5  # 0-1, higher means faster preferred
    ) -> Tuple[str, Dict[str, float]]:
        """Select optimal model for the task."""
        
        # Auto-detect complexity if not provided
        if complexity is None:
            complexity = self._detect_complexity(task_description)
        
        # Get requirements
        requirements = self.complexity_requirements[complexity]
        min_quality = quality_threshold or requirements["min_quality"]
        max_cost = budget_constraint or requirements["max_cost"]
        
        # Filter models that meet requirements
        eligible_models = []
        for name, model in self.models.items():
            if (model.quality_score >= min_quality and 
                model.cost_per_mtok <= max_cost and
                model.context_length >= context_length):
                eligible_models.append((name, model))
        
        if not eligible_models:
            # Fallback to best affordable model
            affordable = [(n, m) for n, m in self.models.items() 
                         if m.cost_per_mtok <= max_cost]
            if affordable:
                eligible_models = [max(affordable, key=lambda x: x[1].quality_score)]
            else:
                eligible_models = [("claude-3-5-haiku", self.models["claude-3-5-haiku"])]
        
        # Score and rank eligible models
        scored_models = []
        for name, model in eligible_models:
            score = self._calculate_model_score(
                model, min_quality, max_cost, speed_priority
            )
            
            # Adjust score based on performance history
            if self.performance_history[name]:
                avg_performance = np.mean(self.performance_history[name][-10:])
                score *= avg_performance
            
            scored_models.append((name, model, score))
        
        # Select best model
        best_model = max(scored_models, key=lambda x: x[2])
        selected_name = best_model[0]
        
        # Calculate decision metrics
        decision_metrics = {
            "complexity": complexity.value,
            "quality_score": best_model[1].quality_score,
            "cost_per_mtok": best_model[1].cost_per_mtok,
            "speed_score": best_model[1].speed_score,
            "overall_score": best_model[2],
            "estimated_cost": (context_length / 1_000_000) * best_model[1].cost_per_mtok
        }
        
        return selected_name, decision_metrics
    
    def _detect_complexity(self, task_description: str) -> TaskComplexity:
        """Auto-detect task complexity from description."""
        task_lower = task_description.lower()
        
        # Simple heuristics - in practice, use ML model
        complex_keywords = ["analyze", "research", "design", "architect", "complex", "critical"]
        moderate_keywords = ["implement", "create", "build", "develop", "modify"]
        simple_keywords = ["format", "convert", "list", "simple", "basic", "check"]
        
        if any(kw in task_lower for kw in complex_keywords):
            if "critical" in task_lower or "urgent" in task_lower:
                return TaskComplexity.CRITICAL
            return TaskComplexity.COMPLEX
        elif any(kw in task_lower for kw in moderate_keywords):
            return TaskComplexity.MODERATE
        elif any(kw in task_lower for kw in simple_keywords):
            return TaskComplexity.SIMPLE
        else:
            return TaskComplexity.MODERATE
    
    def _calculate_model_score(
        self, 
        model: ModelCapability,
        min_quality: float,
        max_cost: float,
        speed_priority: float
    ) -> float:
        """Calculate overall score for model selection."""
        # Normalize scores
        quality_score = (model.quality_score - min_quality) / (1 - min_quality)
        cost_score = 1 - (model.cost_per_mtok / max_cost)
        speed_score = model.speed_score
        
        # Weight factors
        quality_weight = 0.4 * (1 - speed_priority)
        cost_weight = 0.3
        speed_weight = 0.3 * speed_priority
        
        # Calculate weighted score
        score = (
            quality_score * quality_weight +
            cost_score * cost_weight +
            speed_score * speed_weight
        )
        
        return score
    
    def update_performance(self, model_name: str, success: bool, quality_rating: float):
        """Update model performance history for adaptive selection."""
        if model_name in self.performance_history:
            # Combine success and quality into performance score
            performance_score = (0.7 * float(success) + 0.3 * quality_rating)
            self.performance_history[model_name].append(performance_score)
            
            # Keep only recent history
            if len(self.performance_history[model_name]) > 100:
                self.performance_history[model_name] = self.performance_history[model_name][-100:]
    
    def get_recommendation_report(self) -> Dict[str, any]:
        """Generate model usage recommendations."""
        recommendations = {}
        
        for model_name, history in self.performance_history.items():
            if history:
                avg_performance = np.mean(history)
                recent_performance = np.mean(history[-10:]) if len(history) >= 10 else avg_performance
                
                trend = "improving" if recent_performance > avg_performance else "declining"
                
                recommendations[model_name] = {
                    "avg_performance": round(avg_performance, 3),
                    "recent_performance": round(recent_performance, 3),
                    "trend": trend,
                    "usage_count": len(history),
                    "recommendation": self._generate_recommendation(
                        model_name, avg_performance, trend
                    )
                }
        
        return recommendations
    
    def _generate_recommendation(self, model_name: str, performance: float, trend: str) -> str:
        """Generate specific recommendation for model usage."""
        if performance > 0.85:
            return f"Continue using {model_name} for complex tasks"
        elif performance > 0.7:
            if trend == "improving":
                return f"Increase usage of {model_name} for moderate tasks"
            else:
                return f"Monitor {model_name} performance closely"
        else:
            return f"Consider replacing {model_name} with higher-tier model"

4. Agent Communication Optimization

interface Message {
  id: string;
  from: string;
  to: string;
  content: any;
  timestamp: number;
  tokens: number;
}
 
interface CommunicationChannel {
  id: string;
  agents: string[];
  messageHistory: Message[];
  compressionEnabled: boolean;
  batchingEnabled: boolean;
}
 
class AgentCommunicationOptimizer {
  private channels: Map<string, CommunicationChannel> = new Map();
  private messageQueue: Map<string, Message[]> = new Map();
  private compressionRatio = 0.7; // Typical compression ratio
  
  constructor(
    private batchInterval: number = 1000,
    private batchSizeThreshold: number = 5
  ) {
    // Process batched messages periodically
    setInterval(() => this.processBatchedMessages(), this.batchInterval);
  }
  
  createChannel(agents: string[], options: {
    compressionEnabled?: boolean;
    batchingEnabled?: boolean;
  } = {}) {
    const channelId = this.generateChannelId(agents);
    
    this.channels.set(channelId, {
      id: channelId,
      agents,
      messageHistory: [],
      compressionEnabled: options.compressionEnabled ?? true,
      batchingEnabled: options.batchingEnabled ?? true
    });
    
    return channelId;
  }
  
  async sendMessage(
    from: string,
    to: string,
    content: any,
    urgent: boolean = false
  ) {
    const channelId = this.getChannelId(from, to);
    const channel = this.channels.get(channelId);
    
    if (!channel) {
      throw new Error(`No channel exists between ${from} and ${to}`);
    }
    
    const message: Message = {
      id: this.generateMessageId(),
      from,
      to,
      content,
      timestamp: Date.now(),
      tokens: this.estimateTokens(content)
    };
    
    if (urgent || !channel.batchingEnabled) {
      // Send immediately
      await this.deliverMessage(message, channel);
    } else {
      // Queue for batching
      this.queueMessage(channelId, message);
    }
  }
  
  private queueMessage(channelId: string, message: Message) {
    if (!this.messageQueue.has(channelId)) {
      this.messageQueue.set(channelId, []);
    }
    
    this.messageQueue.get(channelId)!.push(message);
    
    // Check if batch size threshold reached
    if (this.messageQueue.get(channelId)!.length >= this.batchSizeThreshold) {
      this.processBatch(channelId);
    }
  }
  
  private async processBatchedMessages() {
    for (const [channelId, messages] of this.messageQueue) {
      if (messages.length > 0) {
        await this.processBatch(channelId);
      }
    }
  }
  
  private async processBatch(channelId: string) {
    const channel = this.channels.get(channelId);
    if (!channel) return;
    
    const messages = this.messageQueue.get(channelId) || [];
    if (messages.length === 0) return;
    
    // Clear queue
    this.messageQueue.set(channelId, []);
    
    // Combine messages
    const batchedMessage = this.combineMessages(messages, channel);
    
    // Deliver as single message
    await this.deliverMessage(batchedMessage, channel);
  }
  
  private combineMessages(messages: Message[], channel: CommunicationChannel): Message {
    const combinedContent = messages.map(m => ({
      id: m.id,
      content: m.content,
      timestamp: m.timestamp
    }));
    
    let content = combinedContent;
    let tokens = messages.reduce((sum, m) => sum + m.tokens, 0);
    
    if (channel.compressionEnabled) {
      // Apply compression (simplified)
      content = this.compressContent(combinedContent);
      tokens = Math.floor(tokens * this.compressionRatio);
    }
    
    return {
      id: this.generateMessageId(),
      from: messages[0].from,
      to: messages[0].to,
      content: {
        type: 'batch',
        messages: content,
        count: messages.length
      },
      timestamp: Date.now(),
      tokens
    };
  }
  
  private compressContent(content: any): any {
    // Simplified compression - in practice, use proper compression
    return {
      compressed: true,
      data: JSON.stringify(content),
      originalSize: JSON.stringify(content).length,
      compressedSize: Math.floor(JSON.stringify(content).length * this.compressionRatio)
    };
  }
  
  private async deliverMessage(message: Message, channel: CommunicationChannel) {
    // Add to history
    channel.messageHistory.push(message);
    
    // Simulate delivery
    console.log(`Delivered message from ${message.from} to ${message.to}: ${message.tokens} tokens`);
    
    // Emit event for actual delivery
    this.emit('message_delivered', message);
  }
  
  optimizeCommunicationPatterns(): Map<string, any> {
    const patterns = new Map<string, any>();
    
    for (const [channelId, channel] of this.channels) {
      const analysis = this.analyzeChannel(channel);
      patterns.set(channelId, analysis);
    }
    
    return patterns;
  }
  
  private analyzeChannel(channel: CommunicationChannel) {
    const messages = channel.messageHistory;
    if (messages.length === 0) return null;
    
    // Calculate metrics
    const totalTokens = messages.reduce((sum, m) => sum + m.tokens, 0);
    const avgTokensPerMessage = totalTokens / messages.length;
    
    // Find communication patterns
    const messagesByHour = new Map<number, number>();
    messages.forEach(m => {
      const hour = new Date(m.timestamp).getHours();
      messagesByHour.set(hour, (messagesByHour.get(hour) || 0) + 1);
    });
    
    const peakHour = Array.from(messagesByHour.entries())
      .sort((a, b) => b[1] - a[1])[0];
    
    // Generate recommendations
    const recommendations = [];
    
    if (avgTokensPerMessage < 100 && !channel.batchingEnabled) {
      recommendations.push("Enable batching for small messages");
    }
    
    if (totalTokens > 10000 && !channel.compressionEnabled) {
      recommendations.push("Enable compression for high-volume channel");
    }
    
    if (peakHour && peakHour[1] > messages.length * 0.3) {
      recommendations.push(`Schedule non-urgent messages outside peak hour ${peakHour[0]}`);
    }
    
    return {
      totalMessages: messages.length,
      totalTokens,
      avgTokensPerMessage: Math.round(avgTokensPerMessage),
      compressionSavings: channel.compressionEnabled ? 
        Math.round(totalTokens * (1 - this.compressionRatio)) : 0,
      peakHour: peakHour ? peakHour[0] : null,
      recommendations
    };
  }
  
  private generateChannelId(agents: string[]): string {
    return agents.sort().join('-');
  }
  
  private getChannelId(from: string, to: string): string {
    return this.generateChannelId([from, to]);
  }
  
  private generateMessageId(): string {
    return `msg_${Date.now()}_${Math.random().toString(36).substr(2, 9)}`;
  }
  
  private estimateTokens(content: any): number {
    const text = JSON.stringify(content);
    return Math.ceil(text.length / 4);
  }
  
  private emit(event: string, data: any) {
    // Event emitter implementation
    console.log(`Event: ${event}`, data);
  }
}

Cost Analysis and ROI

class MultiAgentCostAnalyzer:
    """Analyzes costs and ROI for multi-agent optimizations."""
    
    def __init__(self):
        self.baseline_costs = {}
        self.optimized_costs = {}
        self.optimization_metrics = {}
    
    def analyze_optimization_impact(
        self,
        agents: List[str],
        baseline_usage: Dict[str, Dict],
        optimized_usage: Dict[str, Dict],
        implementation_cost: float = 0
    ) -> Dict[str, any]:
        """Analyze the impact of optimizations."""
        
        # Calculate baseline costs
        baseline_total = 0
        for agent in agents:
            if agent in baseline_usage:
                agent_cost = self._calculate_agent_cost(baseline_usage[agent])
                self.baseline_costs[agent] = agent_cost
                baseline_total += agent_cost
        
        # Calculate optimized costs
        optimized_total = 0
        for agent in agents:
            if agent in optimized_usage:
                agent_cost = self._calculate_agent_cost(optimized_usage[agent])
                self.optimized_costs[agent] = agent_cost
                optimized_total += agent_cost
        
        # Calculate savings
        total_savings = baseline_total - optimized_total
        savings_percent = (total_savings / baseline_total * 100) if baseline_total > 0 else 0
        
        # Calculate ROI
        monthly_savings = total_savings * 30  # Assuming daily costs
        annual_savings = monthly_savings * 12
        roi_months = implementation_cost / monthly_savings if monthly_savings > 0 else float('inf')
        
        # Optimization breakdown
        optimization_impact = {
            "context_sharing": self._calculate_context_sharing_impact(baseline_usage, optimized_usage),
            "model_selection": self._calculate_model_selection_impact(baseline_usage, optimized_usage),
            "request_batching": self._calculate_batching_impact(baseline_usage, optimized_usage),
            "caching": self._calculate_caching_impact(baseline_usage, optimized_usage)
        }
        
        return {
            "baseline_cost": round(baseline_total, 2),
            "optimized_cost": round(optimized_total, 2),
            "total_savings": round(total_savings, 2),
            "savings_percent": round(savings_percent, 1),
            "monthly_savings": round(monthly_savings, 2),
            "annual_savings": round(annual_savings, 2),
            "roi_months": round(roi_months, 1),
            "optimization_breakdown": optimization_impact,
            "per_agent_analysis": self._get_per_agent_analysis()
        }
    
    def _calculate_agent_cost(self, usage: Dict) -> float:
        """Calculate cost for single agent."""
        input_cost = (usage.get("input_tokens", 0) / 1_000_000) * usage.get("input_rate", 3.0)
        output_cost = (usage.get("output_tokens", 0) / 1_000_000) * usage.get("output_rate", 15.0)
        cache_cost = (usage.get("cache_tokens", 0) / 1_000_000) * 0.30
        
        return input_cost + output_cost + cache_cost
    
    def _calculate_context_sharing_impact(self, baseline: Dict, optimized: Dict) -> Dict:
        """Calculate impact of context sharing optimization."""
        baseline_context = sum(
            agent.get("input_tokens", 0) for agent in baseline.values()
        )
        optimized_context = sum(
            agent.get("input_tokens", 0) for agent in optimized.values()
        )
        
        reduction = baseline_context - optimized_context
        reduction_percent = (reduction / baseline_context * 100) if baseline_context > 0 else 0
        
        return {
            "tokens_saved": reduction,
            "reduction_percent": round(reduction_percent, 1),
            "cost_impact": round((reduction / 1_000_000) * 3.0, 2)
        }
    
    def _calculate_model_selection_impact(self, baseline: Dict, optimized: Dict) -> Dict:
        """Calculate impact of dynamic model selection."""
        baseline_models = {}
        optimized_models = {}
        
        for agent, usage in baseline.items():
            model = usage.get("model", "unknown")
            baseline_models[model] = baseline_models.get(model, 0) + 1
        
        for agent, usage in optimized.items():
            model = usage.get("model", "unknown")
            optimized_models[model] = optimized_models.get(model, 0) + 1
        
        return {
            "baseline_distribution": baseline_models,
            "optimized_distribution": optimized_models,
            "shifted_to_cheaper": sum(
                optimized_models.get(m, 0) for m in ["haiku", "claude-3-5-haiku"]
            ) - sum(
                baseline_models.get(m, 0) for m in ["haiku", "claude-3-5-haiku"]
            )
        }
    
    def _calculate_batching_impact(self, baseline: Dict, optimized: Dict) -> Dict:
        """Calculate impact of request batching."""
        baseline_requests = sum(agent.get("request_count", 0) for agent in baseline.values())
        optimized_requests = sum(agent.get("request_count", 0) for agent in optimized.values())
        
        return {
            "requests_reduced": baseline_requests - optimized_requests,
            "reduction_percent": round(
                ((baseline_requests - optimized_requests) / baseline_requests * 100) 
                if baseline_requests > 0 else 0, 1
            )
        }
    
    def _calculate_caching_impact(self, baseline: Dict, optimized: Dict) -> Dict:
        """Calculate impact of caching."""
        cache_tokens = sum(agent.get("cache_tokens", 0) for agent in optimized.values())
        cache_savings = (cache_tokens / 1_000_000) * (3.0 - 0.30)  # Difference between regular and cache cost
        
        return {
            "cache_tokens_used": cache_tokens,
            "cost_savings": round(cache_savings, 2),
            "cache_hit_rate": round(
                (cache_tokens / sum(agent.get("input_tokens", 0) for agent in optimized.values()) * 100)
                if sum(agent.get("input_tokens", 0) for agent in optimized.values()) > 0 else 0, 1
            )
        }
    
    def _get_per_agent_analysis(self) -> Dict[str, Dict]:
        """Get per-agent cost analysis."""
        analysis = {}
        
        for agent in set(list(self.baseline_costs.keys()) + list(self.optimized_costs.keys())):
            baseline = self.baseline_costs.get(agent, 0)
            optimized = self.optimized_costs.get(agent, 0)
            
            analysis[agent] = {
                "baseline_cost": round(baseline, 2),
                "optimized_cost": round(optimized, 2),
                "savings": round(baseline - optimized, 2),
                "savings_percent": round(
                    ((baseline - optimized) / baseline * 100) if baseline > 0 else 0, 1
                )
            }
        
        return analysis

Implementation Checklist

Phase 1: Foundation (Week 1-2)

• Implement shared context manager
• Set up basic agent orchestration
• Create cost tracking infrastructure
• Deploy monitoring dashboard

Phase 2: Optimization (Week 3-4)

• Implement dynamic model selection
• Enable request batching and queuing
• Set up context caching strategies
• Configure agent communication channels

Phase 3: Intelligence (Week 5-6)

• Deploy ML-based optimization
• Implement predictive scaling
• Enable advanced analytics
• Create cost anomaly detection

Phase 4: Refinement (Week 7-8)

• Fine-tune optimization parameters
• Implement A/B testing framework
• Create performance benchmarks
• Generate ROI reports

Conclusion

Multi-agent cost optimization requires a holistic approach combining:

1. Shared Resources: Minimize redundant processing through context sharing
2. Intelligent Orchestration: Coordinate agents to prevent resource waste
3. Dynamic Adaptation: Adjust strategies based on real-time performance
4. Continuous Monitoring: Track and optimize costs at every level

With proper implementation, multi-agent systems can achieve 70-90% cost reduction while maintaining or improving performance.