Multi-Agent

The core advantage of multi-agent workflows is that each Agent can use a lighter-weight and more specialized model, making the overall system's reliability and controllability superior to solutions that rely on a single all-purpose large model.

Transportation systems are a typical multi-agent scenario, where each driver, bus, and traffic light makes local decisions, and the overall effect depends on the superposition of those decisions.

应只在出现特定协调需求时才增加第二个智能体，大多数需要多智能体的问题实际上是需要更窄工具和更好评估的问题

For tasks with relatively linear workflows, a single-model pipeline with structured tool-calling and strict output validation is cleaner, more controllable, and less prone to hallucination than a multi-agent system.

Multi-agent architectures introduce a Trust Propagation attack surface, where content transmitted by a compromised agent may carry malicious instructions, constituting a lateral movement attack.

In multi-agent architectures, messages sent from one agent to another should be protected in the same way as user input, and each agent should only possess the permissions necessary to run its tools.

Multi-agent systems should only be considered after a single agent is working correctly, because inter-agent communication means more tokens, more drift, and harder debugging.

多智能体架构相比单一大模型对话能从不同角度施压，减少某一盲区被忽略的概率

Premortem采用多智能体架构，将质疑任务分解给多个专门化的AI角色，分别质疑市场规模、财务假设、竞争对手视角等

The core difference between workflows and multi-agent systems lies in the trade-off between determinism and autonomy.

Multi-agent systems adopt a modular design, so adding new features or agents only requires adding new modules without restructuring the entire system.

For complex business automation scenarios, multi-agent systems are considered more suitable than a single oversized model.

Multi-agent, multi-round verification workflows entail a large number of LLM calls, and computational costs cannot be ignored.

WorldClaw achieves 3D world generation through a multi-agent collaborative architecture.

The quality of task decomposition—how high a proportion can be parallelized—directly determines the actual benefit of a multi-agent solution.

如果单Agent加合理的工具调用就能解决问题，则不必采用会导致Token消耗成倍增长的多智能体架构

多 Agent 共享同一环境可减少数据传输序列化开销，但带来权限隔离和资源竞争的工程挑战