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name: multi_agent_patterns router_kit: AIKit description: Çoklu agent mimarisi tasarımı, orchestration patterns ve agent collaboration rehberi. metadata: skillport: category: architecture tags: [agents, algorithms, artificial intelligence, automation, chatbots, cognitive services, deep learning, embeddings, frameworks, generative ai, inference, large language models, llm, machine learning, model fine-tuning, multi agent patterns, natural language processing, neural networks, nlp, openai, prompt engineering, rag, retrieval augmented generation, tools, vector databases, workflow automation] - patterns

🤖 Multi-Agent Patterns

Çoklu agent mimarisi ve orchestration rehberi.

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📋 Ne Zaman Multi-Agent?

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🏗️ Mimari Patterns

1. Orchestrator Pattern

Copy        ┌─────────────┐
        │ Orchestrator│
        └──────┬──────┘
               │
    ┌──────────┼──────────┐
    ▼          ▼          ▼
┌───────┐  ┌───────┐  ┌───────┐
│Agent 1│  │Agent 2│  │Agent 3│
│Coder  │  │Tester │  │Reviewer│
└───────┘  └───────┘  └───────┘

Kullanım: Complex workflows, task delegation

2. Pipeline Pattern

Copy┌───────┐    ┌───────┐    ┌───────┐
│ Parse │ -> │Process│ -> │ Output│
└───────┘    └───────┘    └───────┘

Kullanım: Sequential processing, data transformation

3. Specialist Pattern

Copy        ┌─────────────┐
        │   Router    │
        └──────┬──────┘
               │ (task type)
    ┌──────────┼──────────┐
    ▼          ▼          ▼
┌───────┐  ┌───────┐  ┌───────┐
│  SQL  │  │  API  │  │  UI   │
│Expert │  │Expert │  │Expert │
└───────┘  └───────┘  └───────┘

Kullanım: Domain-specific expertise

4. Debate Pattern

Copy┌───────┐         ┌───────┐
│Agent A│ <-----> │Agent B│
│(Pro)  │ debate  │(Con)  │
└───────┘         └───────┘
         \       /
          \     /
           \   /
        ┌───────┐
        │ Judge │
        └───────┘

Kullanım: Decision making, option evaluation

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🔧 Implementation

Agent Definition

Copyclass Agent:
    def __init__(self, name, role, skills):
        self.name = name
        self.role = role
        self.skills = skills
    
    def process(self, task):
        # Agent logic
        pass

Orchestrator

Copyclass Orchestrator:
    def __init__(self, agents):
        self.agents = agents
    
    def route(self, task):
        # Determine which agent handles task
        agent = self.select_agent(task)
        return agent.process(task)
    
    def select_agent(self, task):
        # Routing logic
        pass

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📊 Communication Patterns

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⚠️ Best Practices

1. Clear Roles: Her agent'ın net görevi olsun
2. Minimal Overlap: Görev çakışması olmasın
3. Fallback: Agent fail olursa plan B
4. Monitoring: Her agent'ı izle
5. Context Sharing: Gerekli bilgiyi paylaş

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Multi-Agent Patterns v1.1 - Enhanced

🔄 Workflow

Kaynak: AutoGen Documentation & CrewAI

Aşama 1: Role Definition

• Persona Design: Her agent için net bir "System Message" yaz (Sen nesin, ne yaparsın, ne yapmazsın).
• Tools: Agent'a sadece ihtiyacı olan tool'ları ver (LLM'in halüsinasyon riskini azaltır).
• Hierarchy: Kim kime rapor verecek? (Manager -> Worker) yoksa (Peer-to-Peer) mi?

Aşama 2: Interaction Pattern

• Chat Topology: "Group Chat" (Herkes konuşur) vs "Nested Chats" (Alt gruplar) seçimi yap.
• Handoffs: Görev devir teslimi için açık tetikleyiciler (Trigger phrases) belirle.
• Human-in-the-loop: Kritik kararlar için "User Proxy Agent" veya onay mekanizması ekle.

Aşama 3: Execution & Output

• Consolidation: Sonuçları birleştiren bir "Summarizer Agent" ata.
• Validation: Çıktının formata uygunluğunu (JSON/Markdown) kontrol eden validator ekle.
• Cost Control: Maksimum tur (Max Turns) ve token limitlerini ayarla.

Kontrol Noktaları