Langchain conducted a test to assess the capabilities of using LLM artificial intelligence in an agentic manner, where AI can access various tools and make decisions on its own. It was found that some models perform poorly when given too many tools to work with.
The test consisted of 30 tasks related to calendar management and customer support, each task running three times, totaling 90 runs. The testing process started by running with only relevant tools and gradually adding more tools to assess performance.
Results showed that OpenAI o1 performed exceptionally well, maintaining high success rates even with increased commands. However, models like GPT-4o saw rapid performance decline when additional commands were added.
Overall, agentic performance can be categorized into two distinct groups: high-performing models like o1, o3-mini, and Claude 3.5 Sonnet, and lower-performing models like GPT-4o and Llama-3.3, which suffer significant performance drops with longer commands.
TLDR: Langchain tested the LLM AI in an agentic mode, revealing varying performance levels among different models when given access to multiple tools.
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