A new diagnostic framework, DiaVLo, is presented for analyzing the behaviors of vision-language models (VLMs). It uses human curation and VLM generation capabilities to identify desired and observed behaviors, and provides causal estimates to determine influential concepts driving VLM behavior. This affects AI agents by improving the reliability of VLMs.