Modern AI-powered tools are actively used for automated website testing: detecting bugs, checking accessibility, analyzing user behavior, and searching for vulnerabilities. However, even advanced models are not perfect and can miss issues or generate false alarms. One of the main reasons is limited contextual understanding. Web pages often depend on dynamic data, asynchronous requests, and third-party services. AI may miss a problem if it only occurs in real time or under specific environmental conditions. Another reason is the ambiguity of errors. Some bugs only manifest themselves in certain scenarios, rare user actions, or specific browser settings. The model may incorrectly classify such situations as normal behavior or miss them entirely. AI also often suffers from training set limitations. If the test data doesn't cover rare cases, the model won't be able to recognize them on a real website. This is especially true for complex validation forms, unusual input forms, or non-standard interface solutions. False positives are a significant factor. Incorrectly configured rules or unexpected combinations of page elements can cause the AI ​​to flag otherwise benign scenarios as errors, which reduces the tool's credibility and requires manual review. Operating conditions and security also influence bug detection. Some bugs only manifest in certain regions, devices, or authentication levels. Without access to such contexts, AI may not notice the issue until a human reproduces it manually. To improve reliability, it's important to combine AI with traditional testing methods: manual checks, functional tests, user session monitoring, and regression audits. Combining these approaches helps reduce the risk of missed tests and lower the false alarm rate. Future developments include improving the models' contextual understanding, integrating them with environment simulators, and creating more representative training datasets. Also, adjusting detection thresholds and periodically recalibrating the systems will help adapt the AI ​​to real-world site conditions.