The VAKRA framework evaluates how AI agents handle complex tool-use tasks and reasoning chains. Researchers identified specific failure modes where agents loop indefinitely or misinterpret tool outputs. This analysis provides a blueprint for debugging agentic workflows. Developers can now target these precise bottlenecks to improve reliability in autonomous LLM systems.