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)

According to Andrew Ng (@AndrewYNg), DeepLearning.AI has launched a new short course, 'Building and Evaluating Data Agents,' in collaboration with Snowflake, taught by @datta_cs and @_jreini. This course addresses the critical issue of silent failures in large language model (LLM) data agents, where agents often provide confident but incorrect answers without clear failure signals (source: Andrew Ng, Twitter, Sep 24, 2025). The curriculum teaches participants to construct reliable LLM data agents using the Goal-Plan-Action framework and integrate runtime evaluations that detect failures during execution. The program emphasizes the use of OpenTelemetry tracing and advanced evaluation infrastructure to pinpoint failure points and systematically enhance agent performance. Learners will also orchestrate multi-step workflows spanning web search, SQL, and document retrieval within LangGraph-based agents. This skillset empowers businesses and AI professionals with precise visibility into every stage of an agent’s reasoning, enabling rapid identification and systematic resolution of operational issues—critical for scaling AI agent deployment in enterprise environments (source: DeepLearning.AI course page).