According to DeepLearning.AI, Anthropic reported that hackers linked to China used its Claude Code agentic system to conduct what is described as the first large-scale cyberattack with minimal human involvement. However, independent security researchers challenge this claim, noting that current AI agents struggle to autonomously execute complex cyberattacks and that only a handful of breaches were achieved out of dozens of attempts. This debate highlights the evolving capabilities of AI-powered cybersecurity threats and underscores the need for businesses to assess the actual risks posed by autonomous AI agents. Verified details suggest the practical impact remains limited, but the event signals a growing trend toward the use of generative AI in cyber operations, prompting organizations to strengthen AI-specific security measures. (Source: DeepLearning.AI, The Batch)

According to Andrew Ng (@AndrewYNg), the single most important factor influencing the speed of progress in building AI agents is a team's ability to implement disciplined processes for evaluations (evals) and error analysis. Ng emphasizes that while it might be tempting to quickly address surface-level mistakes, a structured approach to measuring system performance and identifying root causes of errors leads to significantly faster, more sustainable progress in developing agentic AI systems. He notes that traditional supervised learning offers standard metrics like accuracy and F1, but generative and agentic AI systems pose new challenges due to a much wider range of possible errors. The recommended best practice is to prototype quickly, manually inspect outputs, and iteratively refine both datasets and evaluation metrics—including using LLMs as judges where appropriate. This approach enables teams to precisely measure improvements and better target development efforts, which is crucial for enterprise AI adoption and scaling. These insights are shared in depth in Module 4 of the Agentic AI course on deeplearning.ai (source: Andrew Ng, deeplearning.ai/the-batch/issue-323/).