Multi-Agent Architecture
Core Facts
Timeline (last 90 days)
In a multi-agent architecture, downstream agents can only obtain summaries and fragments from upstream agents, resulting in significant information loss during transmission.
Duos adopts a multi-agent technology stack at the technical level.
Compared to a single large-model solution, a multi-agent architecture generally has advantages in role consistency, strategy switching, and structured feedback.
A pragmatic compromise is to maintain the controllability of fixed graphs at the macro process level while introducing limited dynamic delegation capabilities at specific nodes.
Agent systems are inherently asynchronous, pausable, and resumable; therefore, distributed consistency issues such as stale reads genuinely exist and need to be addressed through version verification, optimistic locking, and content hashing.
All Facts (8)
The theoretical roots of multi-agent systems can be traced back to distributed artificial intelligence research in the 1980s.
90%UnverifiedWhen an Agent-type Hook is triggered, the system launches an independent subagent to perform complex multi-step tasks, and this subagent has its own independent context and tool-calling capabilities.
70%UnverifiedIn a multi-agent architecture, downstream agents can only obtain summaries and fragments from upstream agents, resulting in significant information loss during transmission.
50%UnverifiedDuos adopts a multi-agent technology stack at the technical level.
50%UnverifiedCompared to a single large-model solution, a multi-agent architecture generally has advantages in role consistency, strategy switching, and structured feedback.
50%UnverifiedA pragmatic compromise is to maintain the controllability of fixed graphs at the macro process level while introducing limited dynamic delegation capabilities at specific nodes.
50%UnverifiedAgent systems are inherently asynchronous, pausable, and resumable; therefore, distributed consistency issues such as stale reads genuinely exist and need to be addressed through version verification, optimistic locking, and content hashing.
50%Unverified多智能体协作框架允许并行处理、相互验证和迭代纠错，可降低模型产生幻觉的概率
50%