Tool Calling in Inference

This post details how inference providers directly influence tool calling success in AI agents. It breaks down the process into three key areas: pre-processing (chat template validation, model prompting), model execution (structured outputs, quantization techniques), and post-processing (parsing). The post emphasizes that while models are trained for tool calling, the inference provider's implementation at each stage is critical for reliability and quality. It also highlights the importance of benchmarking inference providers beyond model quality, focusing on aspects like schema validity, turn efficiency, and end-to-end latency for agentic workloads.