The release of four new test environments aims to benchmark large language models' (LLMs) ability to effectively use tools for task completion, focusing on skills essential for agentic workflows such as planning, function calling, and overriding pre-trained biases. The tests, including tasks like the Typewriter (single and 26 tools), Relational Data, and Multiverse Math, reveal that while models like GPT-4 excel in some areas, they struggle with others, particularly when tasks require multi-step reasoning or deviate from pre-training patterns. Findings indicate that even high-performing models can fail on seemingly simple tasks when faced with complex tool usage scenarios, and reliability issues such as frequent server errors from model providers pose challenges for consistent performance. The research underscores the need for open-source function calling models with improved multi-step task handling and highlights the importance of validating LLMs against specific task requirements before deployment.