Multi-agent systems (MAS) organize multiple specialized AI agents — typically powered by large language models — to collaborate on complex tasks through structured coordination patterns. By 2025, multi-agent architectures have become the dominant approach for building sophisticated AI applications, outperforming single-agent systems in parallel processing, adaptability, and collective reasoning.
A primary agent acts as a hub, decomposing tasks, routing them to specialized workers, and synthesizing results. This hub-and-spoke model ensures consistency and simple debugging, but can bottleneck at 10-20 agents due to coordination overhead and context concentration.
Variants include the Single Agent + Dynamic Routing pattern, where the primary agent invokes specialists via dynamic function calling based on the current task.
Agents collaborate directly without a central authority, enabling flexible local optimizations like negotiation. For example, driver agents might share route information in a delivery system. Peer-to-peer supports emergent behaviors but risks inconsistency without oversight.
A multi-level structure with supervisor agents at the top, domain agents in the middle, and specialists at the bottom. Combines parallel execution, feedback loops, and shared tools/RAG for knowledge access. The control flow follows: decompose, execute parallel subtasks, gather feedback/refine, aggregate results.
| Pattern | Strengths | Weaknesses | Best For |
|---|---|---|---|
| Supervisor | Guaranteed consistency, simple debugging | Orchestrator bottleneck, sequential latency | Task routing, research pipelines |
| Peer-to-Peer | Tactical flexibility, high throughput | Potential inconsistency | Local optimizations, negotiations |
| Hierarchical | Scalable, parallel processing | Complex state management | Multi-domain problems, content creation |
| Hybrid | Strategic + tactical balance | Implementation complexity | Enterprise workflows |
Agents can engage in debate by proposing solutions, critiquing peers, and iterating via feedback loops. A voting mechanism selects the best output from multiple candidates. This pattern improves reasoning through diverse perspectives and is often implemented as:
The Mixture of Agents approach uses diverse specialist agents to achieve collective intelligence that exceeds any individual agent's capability.
Role-specialized agents (researcher, writer, coder, reviewer) leverage domain expertise while using smaller, more efficient models. Key enablers include:
# Supervisor pattern using LangGraph from langgraph.graph import StateGraph, END from typing import TypedDict, Literal class TeamState(TypedDict): task: str research: str code: str review: str status: str def supervisor(state: TeamState) -> TeamState: """Supervisor decomposes task and routes to specialists.""" if not state.get('research'): return {**state, 'status': 'needs_research'} elif not state.get('code'): return {**state, 'status': 'needs_coding'} elif not state.get('review'): return {**state, 'status': 'needs_review'} return {**state, 'status': 'complete'} def researcher(state: TeamState) -> TeamState: result = llm.invoke(f"Research: {state['task']}") return {**state, 'research': result} def coder(state: TeamState) -> TeamState: result = llm.invoke(f"Code solution based on: {state['research']}") return {**state, 'code': result} def reviewer(state: TeamState) -> TeamState: result = llm.invoke(f"Review code: {state['code']}") return {**state, 'review': result} def route(state: TeamState) -> Literal['researcher', 'coder', 'reviewer', '__end__']: status = state['status'] if status == 'needs_research': return 'researcher' if status == 'needs_coding': return 'coder' if status == 'needs_review': return 'reviewer' return '__end__' graph = StateGraph(TeamState) graph.add_node('supervisor', supervisor) graph.add_node('researcher', researcher) graph.add_node('coder', coder) graph.add_node('reviewer', reviewer) graph.set_entry_point('supervisor') graph.add_conditional_edges('supervisor', route) for node in ['researcher', 'coder', 'reviewer']: graph.add_edge(node, 'supervisor') app = graph.compile()