# Agentic AI Resources

A curated collection of resources on Agentic AI, organized by category.

Important Sample Exam Link
{%preview https://claude.ai/public/artifacts/53321c66-4624-4159-ab0b-eddf4a5ef0e7 %}

[Sample Paper Important ](https://claude.ai/public/artifacts/ff8c0d9d-2a02-417f-98ae-23a31a50cbad)
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## 📚 NVIDIA Learning Courses

:::spoiler Building Agentic AI Applications (C-FX-25)
**Link:** https://learn.nvidia.com/courses/course-detail?course_id=course-v1:DLI+C-FX-25+V1

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:::spoiler Agentic AI Development (S-FX-15)
**Link:** https://learn.nvidia.com/courses/course-detail?course_id=course-v1:DLI+S-FX-15+V1

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:::spoiler Advanced Agentic AI (S-FX-32)
**Link:** https://learn.nvidia.com/courses/course-detail?course_id=course-v1:DLI+S-FX-32+V1

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:::spoiler AI Infrastructure Course (C-FX-18)
**Link:** https://learn.nvidia.com/courses/course-detail?course_id=course-v1:DLI+C-FX-18+V1

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:::spoiler LLM Development (C-FX-26)
**Link:** https://learn.nvidia.com/courses/course-detail?course_id=course-v1:DLI+C-FX-26+V1

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:::spoiler Foundation Models (C-FX-15)
**Link:** https://learn.nvidia.com/courses/course-detail?course_id=course-v1:DLI+C-FX-15+V1

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## 🏭 NVIDIA Documentation & Whitepapers

:::spoiler Agentic AI in the Factory
**Link:** https://docs.nvidia.com/ai-enterprise/planning-resource/ai-factory-white-paper/latest/agentic-ai-in-the-factory.html

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:::spoiler Triton Inference Server Optimization
**Link:** https://docs.nvidia.com/deeplearning/triton-inference-server/user-guide/docs/user_guide/optimization.html

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:::spoiler NVIDIA AIQ Toolkit Documentation
**Link:** https://docs.nvidia.com/aiqtoolkit/latest/index.html

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:::spoiler AIQ Toolkit Tutorials
**Link:** https://docs.nvidia.com/aiqtoolkit/latest/tutorials/index.html

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:::spoiler AIQ Toolkit FAQ
**Link:** https://docs.nvidia.com/aiqtoolkit/latest/resources/faq.html

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:::spoiler NeMo Agent Toolkit Quick Start
**Link:** https://docs.nvidia.com/nemo/agent-toolkit/1.1/quick-start/launching-ui.html

**Notes:**
- Go through this page and subpages Completely at a very high level
- https://docs.nvidia.com/nemo/agent-toolkit/latest/index.html
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:::spoiler TensorRT Best Practices
**Link:** https://docs.nvidia.com/deeplearning/tensorrt/latest/performance/best-practices.html

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:::spoiler Triton Dynamic Batching
**Link:** https://docs.nvidia.com/deeplearning/triton-inference-server/user-guide/docs/user_guide/batcher.html

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:::spoiler Triton Backend Documentation
**Link:** https://docs.nvidia.com/deeplearning/triton-inference-server/user-guide/docs/backend/README.html

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:::spoiler NeMo Framework Performance Guide
**Link:** https://docs.nvidia.com/nemo-framework/user-guide/latest/performance/performance-guide.html

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:::spoiler NeMo Data Curation Best Practices
**Link:** https://docs.nvidia.com/nemo-framework/user-guide/latest/datacuration/bestpractices.html

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:::spoiler NeMo RL Documentation
**Link:** https://docs.nvidia.com/nemo/rl/latest/index.html

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:::spoiler GPU Telemetry with Kubernetes
**Link:** https://docs.nvidia.com/datacenter/cloud-native/gpu-telemetry/latest/kube-prometheus.html

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:::spoiler Run:AI NIM Inference
**Link:** https://docs.run.ai/v2.20/Researcher/workloads/inference/nim-inference/

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## 🛠️ NVIDIA Developer Blog Posts

:::spoiler Building Autonomous AI with NVIDIA Agentic NeMo
**Link:** https://medium.com/@zbabar/building-autonomous-ai-with-nvidia-agentic-nemo-e2992ae58cea

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:::spoiler AI Virtual Assistants with NIM Agent Blueprint
**Link:** https://developer.nvidia.com/blog/three-building-blocks-for-creating-ai-virtual-assistants-for-customer-service-with-an-nvidia-nim-agent-blueprint/

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:::spoiler Introduction to LLMs and Prompt Engineering
**Link:** https://developer.nvidia.com/blog/an-introduction-to-large-language-models-prompt-engineering-and-p-tuning/

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:::spoiler Measure AI Workload Performance with DGX Cloud
**Link:** https://developer.nvidia.com/blog/measure-and-improve-ai-workload-performance-with-nvidia-dgx-cloud-benchmarking/

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:::spoiler Scaling LLMs with Triton and TensorRT-LLM
**Link:** https://developer.nvidia.com/blog/scaling-llms-with-nvidia-triton-and-nvidia-tensorrt-llm-using-kubernetes/

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:::spoiler Jamba 1.5 Hybrid Architecture
**Link:** https://developer.nvidia.com/blog/jamba-1-5-llms-leverage-hybrid-architecture-to-deliver-superior-reasoning-and-long-context-handling

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:::spoiler Mastering LLM Inference Optimization
**Link:** https://developer.nvidia.com/blog/mastering-llm-techniques-inference-optimization/

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:::spoiler AI Agents Blueprint Overview
**Link:** https://blogs.nvidia.com/blog/ai-agents-blueprint/

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:::spoiler Improve AI Code Generation with AIQ Toolkit
**Link:** https://developer.nvidia.com/blog/improve-ai-code-generation-using-nvidia-agent-intelligence-toolkit/

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:::spoiler Monitoring ML Models in Production
**Link:** https://developer.nvidia.com/blog/a-guide-to-monitoring-machine-learning-models-in-production

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:::spoiler Building Safer LLM Apps with NeMo Guardrails
**Link:** https://developer.nvidia.com/blog/building-safer-llm-apps-with-langchain-templates-and-nvidia-nemo-guardrails/

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:::spoiler Securing Generative AI with NIM and Guardrails
**Link:** https://developer.nvidia.com/blog/securing-generative-ai-deployments-with-nvidia-nim-and-nvidia-nemo-guardrails/

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## 🧰 NVIDIA Tools & Products

:::spoiler NVIDIA NeMo Guardrails
**Link:** https://developer.nvidia.com/nemo-guardrails

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:::spoiler NeMo Agent Toolkit
**Link:** https://developer.nvidia.com/nemo-agent-toolkit

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:::spoiler Agent Intelligence Toolkit
**Link:** https://developer.nvidia.com/agent-intelligence-toolkit

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:::spoiler NVIDIA Nsight Systems
**Link:** https://developer.nvidia.com/nsight-systems

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:::spoiler NVIDIA AI Overview
**Link:** https://www.nvidia.com/en-us/ai/

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:::spoiler NVIDIA NeMo Platform
**Link:** https://www.nvidia.com/en-us/ai-data-science/products/nemo/

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:::spoiler NVIDIA Learning Contact
**Link:** https://www.nvidia.com/en-us/learn/organizations/contact-us/

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## 📦 GitHub Repositories

:::spoiler NeMo Agent Toolkit Repository
**Link:** https://github.com/NVIDIA/NeMo-Agent-Toolkit

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:::spoiler TensorRT-LLM Repository
**Link:** https://github.com/NVIDIA/TensorRT-LLM

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:::spoiler NeMo Guardrails Repository
**Link:** https://github.com/NVIDIA/NeMo-Guardrails

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:::spoiler AIQ Toolkit Repository
**Link:** https://github.com/NVIDIA/AIQToolkit

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:::spoiler NeMo Framework Repository
**Link:** https://github.com/NVIDIA/NeMo

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## 📊 TensorRT-LLM Documentation

:::spoiler TensorRT-LLM Performance Analysis
**Link:** https://nvidia.github.io/TensorRT-LLM/performance/perf-analysis.html

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:::spoiler TensorRT-LLM Troubleshooting
**Link:** https://nvidia.github.io/TensorRT-LLM/reference/troubleshooting.html

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## 📖 Glossary & Concepts

:::spoiler Multi-Agent Systems
**Link:** https://www.nvidia.com/en-us/glossary/multi-agent-systems/

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:::spoiler Kubernetes Overview
**Link:** https://www.nvidia.com/en-us/glossary/kubernetes/

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:::spoiler Data Flywheel Concept
**Link:** https://www.nvidia.com/en-us/glossary/data-flywheel/

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## 📝 Research Papers (arXiv)

:::spoiler Multi-Agent Framework Paper
**Link:** https://arxiv.org/abs/2502.05439

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:::spoiler Agent Performance Evaluation
**Link:** https://arxiv.org/abs/2310.10501

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:::spoiler Agent Memory Systems
**Link:** https://arxiv.org/abs/2402.02716

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:::spoiler Advanced Agentic AI Design
**Link:** https://arxiv.org/html/2504.15965v1

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## 💼 Industry Applications & Case Studies

:::spoiler Oracle: Agentic AI Overview
**Link:** https://www.oracle.com/artificial-intelligence/agentic-ai/

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:::spoiler Financial Fraud Detection with Multi-Agent Framework
**Link:** https://medium.com/@mariaprokofieva/catch-me-if-you-can-a-multi-agent-framework-for-financial-fraud-detection-03faa7caba7c

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:::spoiler Design Considerations for Real-World Agentic AI
**Link:** https://medium.com/@official.indrajit.kar/design-considerations-of-advanced-agentic-ai-for-real-world-applications-c5ef317ba1e1

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## ⚠️ Challenges & Best Practices

:::spoiler Top 5 Agentic AI Challenges (Confluent)
**Link:** https://www.confluent.io/blog/agentic-ai-the-top-5-challenges-and-how-to-overcome-them/

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:::spoiler 5 Common Pitfalls in Agentic AI Adoption
**Link:** https://www.captechconsulting.com/articles/navigating-the-challenges-5-common-pitfalls-in-agentic-ai-adoption

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:::spoiler AI Agents in Production (Microsoft)
**Link:** https://microsoft.github.io/ai-agents-for-beginners/10-ai-agents-production/

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## ☁️ Azure Architecture Patterns

:::spoiler Transient Fault Handling
**Link:** https://learn.microsoft.com/en-us/azure/architecture/best-practices/transient-faults

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:::spoiler Circuit Breaker Pattern
**Link:** https://learn.microsoft.com/en-us/azure/architecture/patterns/circuit-breaker

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:::spoiler Retry Pattern
**Link:** https://learn.microsoft.com/en-us/azure/architecture/patterns/retry

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## 🧠 AI Agent Memory & Evaluation

:::spoiler AI Agent Memory (IBM)
**Link:** https://www.ibm.com/think/topics/ai-agent-memory

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:::spoiler MCP Agent Memory Types
**Link:** https://www.byteplus.com/en/topic/542179?title=mcp-agent-memory-types-management-implementation

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:::spoiler AI Agent Evaluation (IBM)
**Link:** https://www.ibm.com/think/topics/ai-agent-evaluation

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:::spoiler RAGAS Agent Metrics
**Link:** https://docs.ragas.io/en/stable/concepts/metrics/available_metrics/agents/

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## 🔗 LangChain Resources

:::spoiler Portkey Logging & Tracing
**Link:** https://python.langchain.com/docs/integrations/providers/portkey/logging_tracing_portkey/

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:::spoiler Time-Weighted Retriever
**Link:** https://js.langchain.com/docs/integrations/retrievers/time-weighted-retriever/

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:::spoiler LangChain Tracing Concepts
**Link:** https://python.langchain.com/docs/concepts/tracing/

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:::spoiler Structured Outputs
**Link:** https://python.langchain.com/docs/concepts/structured_outputs/

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:::spoiler LangSmith Rate Limiting
**Link:** https://docs.smith.langchain.com/evaluation/how_to_guides/rate_limiting

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## 📈 Monitoring & Data Quality

:::spoiler Monitoring ML Models (Towards Data Science)
**Link:** https://towardsdatascience.com/monitoring-machine-learning-models-in-production-how-to-track-data-quality-and-integrity-391435c8a299/

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## 🎯 Fine-Tuning & RAG

:::spoiler Making LLMs More Accurate with RAG Fine-Tuning
**Link:** https://towardsdatascience.com/how-to-make-your-llm-more-accurate-with-rag-fine-tuning/

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## 🛡️ Regulatory Compliance & Ethics

:::spoiler FDA AI/ML Software Guidance
**Link:** https://www.fda.gov/medical-devices/software-medical-device-samd/artificial-intelligence-and-machine-learning-software-medical-device

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:::spoiler EU AI Act
**Link:** https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:52021PC0206

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:::spoiler IEEE Ethics in Action
**Link:** https://ethicsinaction.ieee.org/

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:::spoiler AI for Regulatory Compliance
**Link:** https://www.leewayhertz.com/ai-for-regulatory-compliance/#core-technologies

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:::spoiler Responsible AI Updates 2023
**Link:** https://medium.com/@adnanmasood/responsible-ai-revisited-critical-changes-and-updates-since-our-2023-playbook-0c1610d57f37

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:::spoiler AI Guardrails (Aporia)
**Link:** https://www.aporia.com/learn/ai-guardrails/

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## 👥 Human-in-the-Loop

:::spoiler AI Agents with Human-in-the-Loop
**Link:** https://cobusgreyling.medium.com/ai-agents-with-human-in-the-loop-f910d0c0384b

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:::spoiler Human-in-the-Loop AI Explained
**Link:** https://www.holisticai.com/blog/human-in-the-loop-ai#:~:text=Human%2Din%2Dthe%2DLoop,continuous%20feedback%20and%20decision%2Dmaking.

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:::spoiler Key Risks of Unchecked AI
**Link:** https://onereach.ai/blog/human-in-the-loop-agentic-ai-systems/#:~:text=Key%20risks%20of%20unchecked%20AI,Book%20a%20Demo

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## 🎓 Prompt Engineering

:::spoiler Chain-of-Thought Prompting
**Link:** https://www.codecademy.com/article/chain-of-thought-cot-prompting

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## 🚀 Deployment & APIs

:::spoiler Using NVIDIA Llama Nemotron API
**Link:** https://apidog.com/blog/use-nvidia-llama-nemotron-api/

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:::spoiler Deploy Llama 3.1 Nemotron on VM
**Link:** https://dev.to/nodeshiftcloud/how-to-deploy-llama-31-nemotron-70b-instruct-on-a-virtual-machine-in-the-cloud-3777

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## 🎥 Video Resources

:::spoiler NVIDIA Agentic AI Video
**Link:** https://www.youtube.com/watch?v=NaT5Eo97_I0

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## 📌 Quick Reference

**Total Resources:** 108+
**Last Updated:** [Add date when you finish]
**Tags:** #AgenticAI #NVIDIA #MachineLearning #LLM #AIAgents

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*Feel free to add your own notes, ratings, or completion status in each spoiler section!*