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 ====== Single vs Multi-Agent Architectures ====== ====== Single vs Multi-Agent Architectures ======
  
-Choosing between single-agent and multi-agent architectures is a critical design decision that impacts cost, latency, reliability, and maintainability. This guide provides a research-backed framework based on published benchmarks and real-world deployments.+Choosing between single-agent and multi-agent architectures is a critical design decision that impacts cost, latency, reliability, and maintainability. Understanding single agent architecture and how single agent systems compare to multi-agent approaches is essential for building effective AI workflows. This guide provides a research-backed framework based on published benchmarks and real-world deployments.
  
 ===== Overview ===== ===== Overview =====
  
   * **Single Agent** — One LLM-powered agent handles the entire workflow with access to all tools and context. Simple, fast, predictable.   * **Single Agent** — One LLM-powered agent handles the entire workflow with access to all tools and context. Simple, fast, predictable.
-  * **Orchestrator + Specialists** — A central coordinator delegates subtasks to domain-specific agents. Modular, scalable, but adds coordination overhead.+  * **Orchestrator + Specialists(([[https://learn.microsoft.com/en-us/azure/cloud-adoption-framework/ai-agents/single-agent-multiple-agents|Microsoft Azure - Single Agent vs Multiple Agents]]))** — A central coordinator delegates subtasks to domain-specific agents. Modular, scalable, but adds coordination overhead.
   * **Peer-to-Peer** — Autonomous agents collaborate via message passing or shared memory. Maximum parallelism, hardest to debug.   * **Peer-to-Peer** — Autonomous agents collaborate via message passing or shared memory. Maximum parallelism, hardest to debug.
  
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     C -->|No| F{Context window\nsufficient?}     C -->|No| F{Context window\nsufficient?}
     F -->|Yes| E     F -->|Yes| E
-    F -->|No| G[Orchestrator + Specialists]+    F -->|No| G[Orchestrator + Specialists(([[https://learn.microsoft.com/en-us/azure/cloud-adoption-framework/ai-agents/single-agent-multiple-agents|Microsoft Azure - Single Agent vs Multiple Agents]]))]
     D -->|Yes| H{Need real-time\nparallelism?}     D -->|Yes| H{Need real-time\nparallelism?}
     D -->|No| G     D -->|No| G
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 ===== Architecture Comparison ===== ===== Architecture Comparison =====
  
-^ Factor ^ Single Agent ^ Orchestrator + Specialists ^ Peer-to-Peer ^+^ Factor ^ Single Agent ^ Orchestrator + Specialists(([[https://learn.microsoft.com/en-us/azure/cloud-adoption-framework/ai-agents/single-agent-multiple-agents|Microsoft Azure - Single Agent vs Multiple Agents]])) ^ Peer-to-Peer ^
 | **Complexity** | Low | Medium | High | | **Complexity** | Low | Medium | High |
 | **Latency** | Lowest (1 LLM call) | Medium (2-5 LLM calls) | Variable (parallel) | | **Latency** | Lowest (1 LLM call) | Medium (2-5 LLM calls) | Variable (parallel) |
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 **Weaknesses**: Context window limits, single point of failure, struggles with 6+ tools. **Weaknesses**: Context window limits, single point of failure, struggles with 6+ tools.
  
-=== Orchestrator + Specialists ===+=== Orchestrator + Specialists(([[https://learn.microsoft.com/en-us/azure/cloud-adoption-framework/ai-agents/single-agent-multiple-agents|Microsoft Azure - Single Agent vs Multiple Agents]])) ===
  
 A coordinator routes tasks to domain experts. Most common multi-agent pattern. A coordinator routes tasks to domain experts. Most common multi-agent pattern.
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 ^ Framework ^ Pattern ^ Best For ^ Key Feature ^ ^ Framework ^ Pattern ^ Best For ^ Key Feature ^
 | **LangGraph** | Orchestrator | Stateful multi-step workflows | Graph-based state machines | | **LangGraph** | Orchestrator | Stateful multi-step workflows | Graph-based state machines |
-| **CrewAI** | Orchestrator + Specialists | Role-based team workflows | Agent roles and delegation |+| **CrewAI** | Orchestrator + Specialists(([[https://learn.microsoft.com/en-us/azure/cloud-adoption-framework/ai-agents/single-agent-multiple-agents|Microsoft Azure - Single Agent vs Multiple Agents]])) | Role-based team workflows | Agent roles and delegation |
 | **AutoGen** | Peer-to-Peer | Research and collaborative tasks | Dynamic agent conversations | | **AutoGen** | Peer-to-Peer | Research and collaborative tasks | Dynamic agent conversations |
 | **OpenAI Swarm** | Orchestrator | Lightweight agent handoffs | Minimal coordination overhead | | **OpenAI Swarm** | Orchestrator | Lightweight agent handoffs | Minimal coordination overhead |
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   * Multi-agent coding systems score **72.2% on SWE-bench Verified** vs ~65% for single agents using the same base model((([[https://arxiv.org/html/2509.10769v1|AgentArch Benchmark - Evaluating Agent Architectures.]]))),((([[https://redis.io/blog/single-agent-vs-multi-agent-systems/|Redis - Single Agent vs Multi-Agent Systems.]])))   * Multi-agent coding systems score **72.2% on SWE-bench Verified** vs ~65% for single agents using the same base model((([[https://arxiv.org/html/2509.10769v1|AgentArch Benchmark - Evaluating Agent Architectures.]]))),((([[https://redis.io/blog/single-agent-vs-multi-agent-systems/|Redis - Single Agent vs Multi-Agent Systems.]])))
-  * Multi-agent systems show **23% higher accuracy** on complex reasoning tasks+  * Multi-agent systems show **23% higher accuracy** on complex reasoning tasks(([[https://vibecoding.app/blog/multi-agent-vs-single-agent-coding|VibeCoding - Multi-Agent vs Single-Agent Coding]]))
   * Multi-agent throughput improvement of **up to 64%** for parallelizable workloads   * Multi-agent throughput improvement of **up to 64%** for parallelizable workloads
   * Multi-agent token cost is **10-15x higher** for complex requests   * Multi-agent token cost is **10-15x higher** for complex requests
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   - **Default to single agent**. It covers 80% of use cases with lower cost and complexity.   - **Default to single agent**. It covers 80% of use cases with lower cost and complexity.
   - **Add agents for specialization**, not just because you can. Each agent should have a clear, distinct role.   - **Add agents for specialization**, not just because you can. Each agent should have a clear, distinct role.
-  - **Orchestrator + Specialists** is the most practical multi-agent pattern for production.+  - **Orchestrator + Specialists(([[https://learn.microsoft.com/en-us/azure/cloud-adoption-framework/ai-agents/single-agent-multiple-agents|Microsoft Azure - Single Agent vs Multiple Agents]]))** is the most practical multi-agent pattern for production.
   - **Peer-to-peer** is rarely needed outside high-throughput parallel processing.   - **Peer-to-peer** is rarely needed outside high-throughput parallel processing.
   - **Measure the tradeoff**: multi-agent gains 7-23% accuracy but costs 3-15x more tokens.   - **Measure the tradeoff**: multi-agent gains 7-23% accuracy but costs 3-15x more tokens.
- 
-===== References ===== 
- 
-  * [[https://redis.io/blog/single-agent-vs-multi-agent-systems/|Redis - Single Agent vs Multi-Agent Systems]] 
-  * [[https://learn.microsoft.com/en-us/azure/cloud-adoption-framework/ai-agents/single-agent-multiple-agents|Microsoft Azure - Single vs Multiple Agents]] 
-  * [[https://vibecoding.app/blog/multi-agent-vs-single-agent-coding|VibeCoding - Multi-Agent vs Single-Agent Data-Driven Comparison]] 
-  * [[https://arxiv.org/html/2509.10769v1|AgentArch Benchmark - Evaluating Agent Architectures]] 
-  * [[https://www.techaheadcorp.com/blog/single-vs-multi-agent-ai/|TechAhead - Single vs Multi-Agent AI]] 
  
 ===== See Also ===== ===== See Also =====
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   * [[how_to_structure_system_prompts|How to Structure System Prompts]] — Prompt design for agents   * [[how_to_structure_system_prompts|How to Structure System Prompts]] — Prompt design for agents
   * [[how_to_choose_chunk_size|How to Choose Chunk Size]] — RAG optimization   * [[how_to_choose_chunk_size|How to Choose Chunk Size]] — RAG optimization
 +
 +===== References =====
  
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