The VAKRA framework exposes critical gaps in how AI agents handle complex tool-use sequences. Researchers found that agents often fail during multi-step reasoning, despite high performance on simple tasks. This analysis provides a blueprint for debugging agentic workflows. Practitioners can now target specific failure points to improve reliability in autonomous systems.