According to @AndrewYNg, the strongest predictor of how quickly teams advance AI agents is a disciplined process for evals and error analysis rather than ad hoc fixes or chasing buzzy tools, enabling faster, measurable improvement in production systems, source: Andrew Ng on X, Oct 16, 2025. He explains that generative AI expands the output space and failure modes versus supervised learning, making iterative, tailored evals more important than relying solely on standard metrics like accuracy, precision, recall, F1, and ROC, source: Andrew Ng on X, Oct 16, 2025. For enterprise workflows such as automated invoice processing, he recommends rapidly prototyping, manually inspecting outputs, then constructing objective or LLM-as-judge metrics that target high-risk fields like due date, amount, addresses, currency, and API call correctness, source: Andrew Ng on X, Oct 16, 2025. He advises building evals first to quantify system performance and then conducting error analysis to focus development, with detailed guidance in Module 4 of the Agentic AI course and The Batch Issue 323 on deeplearning.ai, source: deeplearning.ai (Agentic AI Module 4; The Batch issue 323, https://www.deeplearning.ai/the-batch/issue-323/).

According to Andrew Ng, the new aisuite capability simplifies function calling with large language models (LLMs), an essential feature for agentic workflows and various LLM applications. This development could enhance efficiency for developers by reducing complexity in tool usage, potentially impacting the speed and effectiveness of AI-driven trading algorithms [source: Andrew Ng Twitter].