Multi-Agent Architecture

The theoretical roots of multi-agent systems can be traced back to distributed artificial intelligence research in the 1980s.

When 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.

Multi-agent collaboration frameworks allow parallel processing, mutual verification, and iterative error correction, which can reduce the probability of models generating hallucinations.

In a multi-agent architecture, token consumption grows linearly or even multiplicatively with the number of agents, because each node needs to carry system prompts, tool definitions, and historical context.

The two researchers adopt a multi-agent architecture rather than a single fully automated digital agent, and treat human researchers and engineers as a special case of agents within the framework, as an indispensable component of the hybrid system.

The advantages of the multi-agent parallel review model include parallelization to shorten latency, isolation to reduce single-point bias, and heterogeneous model combinations to broaden coverage.

In multi-agent architectures, multiple agents each hold independent context windows and cannot directly "see" each other's complete reasoning process, and coordination cost rises roughly linearly with the number of roles.

Multi-agent architectures bring coordination overhead and context-passing loss: each role switch is a lossy compression, and key details may be lost or misunderstood in the retelling.

Compared with multi-agent architectures, the pattern of monolithic bounded tasks plus deterministic verification has a narrower capability boundary, but its behavior is predictable, faults are isolatable and audits are traceable.

Multi-agent architectures perform impressively in demo scenarios but face challenges in enterprise production such as interaction paths that are hard to predict and test, errors that are amplified and hard to localize, and monitoring and audit complexity that rises exponentially with the number of agents.

Architectural complexity must have its justification proven by measurement results, rather than being adopted by default because multi-agent sounds more "advanced".

When judging whether a multi-agent architecture should be used, the core variable is the degree of coupling of the task.

Teams use a parallel architecture when task independence is high, use a sequential pipeline when task coupling is high, and supervisor-worker is a compromise between the two.

In job interviews for agent-related positions, interviewers are increasingly examining Harness Engineering, probing candidates on practical delivery issues such as tool call error handling, data security, high concurrency, multi-tenant isolation, and memory gap remediation.

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.