Human error—a constant factor in complex systems like nuclear power plants, aviation, and even healthcare. Analyzing these errors (known as Human Reliability Analysis or HRA) is crucial for preventing catastrophes, but traditional methods are slow, resource-intensive, and rely heavily on subjective expert opinions. Now, a groundbreaking new framework called KRAIL is leveraging the power of AI and knowledge graphs to transform HRA. KRAIL uses a two-pronged approach. First, it employs a team of AI agents to dissect a scenario, analyzing the task, context, cognitive activities involved, and any time pressures. Second, it taps into a vast knowledge graph built from the IDHEAS-DATA, a comprehensive database of human error information. This allows KRAIL to quickly and accurately estimate the probability of human error, providing invaluable insights for safety improvements. Experiments show KRAIL significantly outperforms manual methods, slashing the time needed for analysis while maintaining high accuracy. This speed boost empowers experts to focus on higher-level decision-making, rather than getting bogged down in tedious calculations. The development of a user-friendly web interface for KRAIL makes this powerful technology accessible to a wider audience. This could lead to a significant improvement in safety across high-risk industries. While promising, KRAIL is still under development. Future research will focus on expanding its knowledge base and fine-tuning its AI models to handle an even wider range of scenarios. This continuous improvement will help ensure that KRAIL becomes an indispensable tool for anyone working to make complex systems safer and more reliable.