Overview
Imagine that all possible answers to a question form a large search space. A single model run samples only a small region. Another run may overlap with the first but also explore a different region. Repeating the process increases coverage.
This is particularly useful for questions such as:
- What are the possible causes of a business problem?
- Which product ideas should be considered?
- What titles or content concepts could work?
- Which strategic options are available?
- What research directions should be explored?
- What could explain an unexpected result?
A single model may return the most statistically common answers. The less obvious possibilities may appear only occasionally. Running several agents creates more opportunities for those lower-probability ideas to emerge.
The same principle can be used at much larger scale. Even if the average agent response is weaker than the reasoning of an exceptional expert, a large fleet can examine many possibilities quickly. The value comes from breadth and parallelism rather than from assuming that every individual answer is excellent.