Benchmarking Single Agent Performance

The study explores the effectiveness of a single ReAct agent architecture in handling tasks across multiple domains, focusing on Calendar Scheduling and Customer Support. It aims to determine how increasing the number of domains affects the agent's performance, specifically when tasked with following instructions and using tools within these domains. The research evaluates several models, including claude-3.5-sonnet, o1, o3-mini, gpt-4o, and llama-3.3-70B, using 30 tasks for each domain, run three times to account for non-deterministic behavior. Results indicate that as more context and tools are introduced, agent performance declines, particularly in tasks that require longer tool-calling trajectories. Models like o1, o3-mini, and claude-3.5-sonnet generally outperformed gpt-4o and llama-3.3-70B, although o3-mini showed a sharp performance drop with increased context. The study suggests that multi-agent architectures may offer improvements over single ReAct agents when managing a large number of domains, and plans to explore this further alongside cross-domain tasks and more complex trajectories.

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