Part 5/8:
Using frameworks like Google’s multi-agent system (MASS), researchers have identified significant performance improvements through these optimization stages:
The initial stage focusing on Block Level Prompt Optimization provides the most substantial advancement, showing performance jumps from 62% to 79%.
The second stage, Workflow Topology Optimization, contributes to incremental gains, rising from 79% to 83%.
The final stage only slightly enhances performance, revealing that initial improvements had already set a strong foundation.
This tiered approach has helped identify the crucial importance of prompt design in achieving successful multi-agent interactions.