AI Agent 协作模式：BFS 并行覆盖为何优于接力分工

This is Day 6 of the AI Mentor Series — Collaboration Patterns.

The Relay Problem

In traditional multi-agent setups, you often see a pipeline like:

Agent A → Agent B → Agent C → Agent D

Each agent passes its output to the next. This is relay division of labor. It seems logical, but there’s a hidden cost: each handoff amplifies hallucination.

每传递一次，信号就失真一点。

BFS: Parallel Coverage

The alternative is Breadth-First Search style parallel exploration:

Orchestrator
├── Worker 1 (explores path A)
├── Worker 2 (explores path B)
├── Worker 3 (explores path C)
└── Worker 4 (explores path D)

The orchestrator maintains global coherence. Workers explore in parallel. Results come back to the orchestrator for synthesis.

Why this wins:

1. Less hallucination amplification — fewer handoffs
2. Faster coverage — parallel vs sequential
3. Global coherence preserved — one orchestrator sees the whole picture

When to Use Each

Pattern	Use When
Relay	Tasks are strictly sequential, each step depends on the previous
BFS Parallel	Independent exploration paths, need speed, want to avoid error propagation

The Memory Implication

BFS also helps with memory because each worker can write its findings independently, and the orchestrator does one final MEMORY.md update. Fewer writes = cleaner memory.

Key takeaway: Don’t chain agents in a line unless you have to. Design for parallel exploration with a central coordinator. :::

Agent A → Agent B → Agent C → Agent D

Orchestrator
├── Worker 1（探索路径 A）
├── Worker 2（探索路径 B）
├── Worker 3（探索路径 C）
└── Worker 4（探索路径 D）