New course: Nvidia's NeMo Agent Toolkit: Making Agents Reliable, taught by Brian McBrayer 🐬 from NVIDIA. Many teams struggle to turn agent demos into reliable systems that are ready for production. This short course teaches you to harden agentic workflows into reliable systems using Nvidia's open-source NeMo Agent Toolkit... (NAT). Whether you built your agent in raw Python or using a framework like LangGraph, or CrewAI, NAT provides building blocks for observability, evaluation, and deployment that turn proofs-of-concept into production-ready systems. NAT makes it easy to troubleshoot and optimize agent performance with execution traces, systematic evaluations, and CI/CD integration. Skills you'll gain: - Build configuration-driven agent workflows with REST APIs and minimal code - Add observability with tracing to visualize agent reasoning and debug performance bottlenecks - Create systematic evaluations using gold-standard datasets to measure and improve agent reliability - Deploy multi-agent systems with authentication, rate limiting, and professional web interfaces - Orchestrate agents from different frameworks to collaborate on complex tasks Join and learn how to turn agent demos into reliable systems!show more

@Pr_Brian @nvidia And in perfect timing for the holidays 🎁

@Pr_Brian @nvidia This is the missing layer for agentic AI, not smarter agents, but measurable, observable, and debuggable ones, reliability is becoming an engineering discipline, not a prompt trick.

@Pr_Brian @nvidia They are again looking at my soul, XD @yacinelearning

@Pr_Brian @nvidia Thank you @Pr_Brian This is the missing layer most teams underestimate. agent = demo() while agent != production_ready: trace() evaluate() fix() Building agents is easy. Making them reliable is the hard part.

The difference between a toy and a tool is observability. We are effectively trying to build deterministic systems on top of probabilistic substrates which means the engineering challenge is no longer about model capability but about guardrails and evaluation. You cannot deploy what you cannot trace.

@Pr_Brian @nvidia You guys are trying so hard to educate us on AI agents workflow we really appreciate it and your efforts will not be in vain keep it up we're together

@Pr_Brian @nvidia Excited to learn how Nvidia's NeMo Agent Toolkit can help build reliable systems!

@Pr_Brian @nvidia Oh

@Pr_Brian @nvidia 🙌

@Pr_Brian @nvidia Agent demos fail in prod for the same reason early microservices did: no observability or contracts. Toolkits like this matter because they force discipline, not because they add “more AI.”

@Pr_Brian @nvidia Wow! 🤩🚀

@Pr_Brian @nvidia Exciting new course! Turning agent demos into reliable systems is key for success. Can't wait to learn more!

@Pr_Brian @nvidia Impressive course on making agents reliable! Excited to learn more.

@Pr_Brian @nvidia Agent demos are easy. Trustworthy agents in production are not. Observability, repeatable evaluations, and deployment hygiene are where most projects stall. Tooling that treats reliability as a first class concern is exactly what the ecosystem needs right now.

@Pr_Brian @nvidia The pursuit of deterministic reliability from a stochastic core is not engineering; it is advanced probabilistic babysitting.

@Pr_Brian @nvidia Harnessing Nvidia's toolkit can indeed transform agentic demos into dependable systems. What challenges do teams face in this transition? 🤔 #AIInnovation

@Pr_Brian @nvidia Exciting course! Turning agent demos into reliable systems is key. Can't wait to learn more!

@Pr_Brian @nvidia Shipping agents breaks down at the reliability layer, not the model layer. Tooling that treats observability, evals, and deployment as first-class citizens is overdue.

@Pr_Brian @nvidia Andrew observes agents require robust workflows for production readiness through new course.

@Pr_Brian @nvidia Reliability is where the rubber meets the road. Demos show potential, but solid frameworks like NAT can make a real difference in scalability and performance. Curious about the specific challenges teams face in implementation.

@Pr_Brian @nvidia Thank you Andrew

@Pr_Brian @nvidia This hits the real gap in agentic AI. Demos are easy. Reliability is hard. Observability, evaluation, and CI/CD are what separate “cool agents” from real systems. Great to see this layer finally getting first-class attention.

The gap between a cool demo and a production-ready system is exactly where most agent projects stall, so this focus on hardening workflows is spot on. I dug into the curriculum, and the approach to observability looks particularly valuable. Using standard tools like OpenTelemetry and Jaeger to trace execution flows is a huge upgrade from staring at print statements when trying to debug complex reasoning loops. It’s also smart that the course covers wrapping existing agents—whether they're built in LangChain or LlamaIndex—into NAT microservices, rather than forcing a total rewrite. Definitely curious to see how Brian approaches the "LLM-as-a-judge" evaluation using Llama 3 70B—automating reliability checks is such a critical piece of the puzzle.

@Pr_Brian @nvidia •Does this reduce cognitive fatigue? •Does this stop wasting my time? •Does this help me think deeper, not louder? •Does this respect my intelligence instead of flattering it? if none of this..is a #Meh product.

This is exactly the gap most teams hit. Demos look great, production breaks 😅 At Eduspere, we’re building a focused learning layer on top of resources like this, so learners don’t just watch agent tutorials but actually build, test, and reason about reliable AI systems step by step. If you’re learning agents and want structure, progress tracking, and AI-assisted guidance while exploring tools like NeMo, join the waitlist → 🚀

@Pr_Brian @nvidia He is still up to date with tech Whats stopping you?

@Pr_Brian @nvidia Agent Reliability Engineering (ARE) is the emerging discipline following SRE:

@Pr_Brian @nvidia IF YOU ARE CURIOUS AND WANT TO LEARN ALL THINGS AI THIS IS THE GUY TO LEARN FROM!!!

@Pr_Brian @nvidia Observability frameworks are table stakes. But when agents autonomously execute multi-chain transactions, who verifies settlement finality actually occurred? That's the infrastructure layer—not tooling—that's still missing.

@Pr_Brian @nvidia Turning agent demos into production systems is the real challenge. Strong observability, evaluation, and CI/CD around agent workflows are key to making AI tools reliable and scalable for real productivity and automation use cases.