According to @AndrewYNg, a new course titled 'Nvidia's NeMo Agent Toolkit: Making Agents Reliable' taught by @Pr_Brian from @NVIDIA addresses a major challenge in the AI industry: turning agent demos into robust, production-ready systems. The course demonstrates how Nvidia's open-source NeMo Agent Toolkit (NAT) enables teams to enhance agentic workflows, regardless of whether agents are built in raw Python, LangGraph, or CrewAI. NAT offers essential modules for observability, evaluation, and deployment, supporting execution trace visualization, systematic performance evaluations, and CI/CD integration. These features streamline the transition from proof-of-concept to reliable production deployment, opening new business opportunities for AI developers and enterprises striving for scalable and dependable agent-based applications (source: @AndrewYNg, Dec 18, 2025).

According to @DeepLearningAI, a new course developed in partnership with Nvidia demonstrates how to improve the reliability of AI agents using the NeMo Agent Toolkit. The course, taught by Brian McBrayer (@Pr_Brian), focuses on addressing common agent demo failures such as unclear tool traces, silent errors, and unintended side effects from code changes. Practical modules cover leveraging OpenTelemetry tracing to pinpoint hidden issues, running automated evaluations to expose brittle reasoning, and deploying workflows that incorporate authentication and rate limiting for consistent behavior in real-world environments. This initiative directly targets the growing demand for robust AI agent applications in production settings, offering business leaders and developers actionable strategies to enhance agent reliability. (Source: @DeepLearningAI, https://twitter.com/DeepLearningAI/status/2001329113622073611)