Research shows ‘more agents’ isn’t a reliable path to better enterprise AI systems

Researchers at Google and MIT have conducted a comprehensive analysis of agentic systems and the dynamics between the number of agents, coordination structure, model capability, and task properties. While the prevailing sentiment in the industry has been "more agents is all you need," the research suggests that scaling agent teams is not a guaranteed path to better performance.

Based on their findings, the researchers have defined a quantitative model that can predict the performance of an agentic system on an unseen task. Their work reveals that adding more agents and tools acts as a double-edged sword: Although it can unlock performance on specific problems, it often introduces unnecessary overhead and diminishing returns on others.

These findings offer a critical roadmap for developers and enterprise decision-makers trying to determine when to deploy complex multi-agent architectures versus simpler, more cost-effective single-agent solutions.

The state of agentic systems

To understand the study's ...