According to DeepLearning.AI (@DeepLearningAI), Andrew Ng emphasized in the latest issue of The Batch that disciplined evaluations followed by systematic error analysis are crucial for accelerating progress in agentic AI systems. This approach helps teams identify bottlenecks and refine models more efficiently, directly impacting the reliability of next-generation AI agents (Source: The Batch, DeepLearning.AI, Oct 20, 2025). The newsletter also highlights significant industry moves: OpenAI is deepening its partnership with AMD to enhance hardware capabilities for AI workloads; DeepSeek is reducing inference prices, making large model deployment more affordable for businesses; Tinker is simplifying multi-GPU fine-tuning, lowering the barrier to advanced AI model optimization; and robotics companies are introducing systems where robots plan pathways visually before movement, improving operational safety and autonomy. These developments signal expanding business opportunities and practical applications across the AI sector, from cost-effective AI deployment to advanced robotics (Source: DeepLearning.AI, Oct 20, 2025).

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/).