The Scaling Dilemma: An Analysis of Tool Quantity and its Impact on ReAct Agent Performance
When enterprise engineering teams scale autonomous agent systems, the default instinct is to expand the agent’s toolset, equipping models with dozens, hundreds, or thousands of API definitions. Empirical benchmarks across state-of-the-art models (including GPT-4o, Claude 3.5 Sonnet, and OpenAI reasoning models) reveal a stark counter-intuitive reality: agent accuracy does not scale with tool count; it collapses. ...