According to Andrew Ng (@AndrewYNg), the increasing adoption of agentic coding in AI-assisted software development has made automated software testing more vital than ever. Agentic testing, where AI systems generate and run tests, is especially effective for infrastructure components, resulting in more stable platforms and fewer downstream bugs (source: deeplearning.ai/the-batch/issue-319/). Ng notes that while coding agents boost productivity, they also introduce new types of errors, including subtle infrastructure bugs and even security loopholes. AI-driven methodologies such as Test Driven Development (TDD) benefit from automation, reducing the manual burden on developers and enhancing reliability. Business opportunities lie in automating rigorous back-end and infrastructure testing, as deep-stack bugs are costly and hard to trace. Companies focusing on agentic testing solutions can address a high-value pain point in the AI software development lifecycle.