According to ElevenLabs (@elevenlabsio), the introduction of Agent Workflows represents a significant advancement in building reliable AI agents by providing teams with fine-grained control over conversation flows (source: x.com/elevenlabsio/status/1975191207149269214). The new visual editor allows businesses to design sophisticated conversational logic by routing tasks to specialized subagents, enabling more complex and customized scenarios. This development is expected to enhance the scalability and maintainability of AI-driven customer service and automation platforms, opening new business opportunities for industries seeking robust AI agent solutions.

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)

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).