By Sergio De Simone
Publication Date: 2026-02-16 09:00:00
Google Research tried to answer the question of how to design agent systems for optimal performance by running a controlled evaluation of 180 agent configurations. From this, the team derived what they call the “first quantitative scaling principles for AI agent systems”, showing that multi-agent coordination does not reliably improve results and can even reduce performance.
The research challenges several widely held beliefs, according to its authors:
Practitioners often rely on heuristics, such as the assumption that “more agents are better”, believing that adding specialized agents will consistently improve results.
Instead, they argue that the benefits hold only for certain classes of tasks, as adding more agents often leads to a performance ceiling and, in some cases, can even hurt performance.
The study evaluates five architectures, including single-agent, independent multi-agent, orchestrated, peer-to-peer, and hybrid systems, and finds that parallelizable…