Uploaded June 2025 | Updated September 2026, 2 weeks ago
Santiago from NVIDIA shares insights, best practices, and lessons learned from building scalable, enterprise-ready AI agents using data flywheels. Drawing from our enterprise generative AI deployments, including chatbots, copilots, and ‘talk-to-your-data’ solutions, we’ll show how we implemented AI agents to orchestrate LLMs, APIs, and workflows for automating multi-step tasks, and data flywheels to drive continuous improvement of LLMs through user feedback. These architectural patterns are key to keeping enterprise AI solutions accurate, scalable, adaptable, and relevant in fast-paced business environments.
Chapters:
0:00 – Introduction and Audience Poll
0:36 – Framing Agents as Digital Employees
1:22 – NV Infobot: Architecture and Use Cases
3:00 – MAPE Framework for Self-Regulating Agents
3:34 – User Feedback Collection Challenges
5:42 – Root Cause Analysis with LLM Assistance
7:32 – Key Error Types and Prioritization
8:59 – Building the Data Flywheel
9:55 – NVIDIA Microservices for Agent Development
11:04 – Developer Workflow and Fine-Tuning
11:37 – Experiment 1: Router Optimization
13:06 – Experiment 2: Query Rephrasal Improvement
14:45 – Summary: Monitor, Analyze, Plan, Execute
15:32 – W&B Blueprint: Deploying Your Own Flywheel
16:44 – Q&A: Error Prioritization and Nemo Tools
19:30 – Closing Remarks and Applause
Santiago from NVIDIA shares insights, best practices, and lessons learned from building scalable, enterprise-ready AI agents using data flywheels. Drawing from our enterprise generative AI deployments, including chatbots, copilots, and ‘talk-to-your-data’ solutions, we’ll show how we implemented AI agents to orchestrate LLMs, APIs, and workflows for automating multi-step tasks, and data flywheels to drive continuous improvement of LLMs through user feedback. These architectural patterns are key to keeping enterprise AI solutions accurate, scalable, adaptable, and relevant in fast-paced business environments.
Chapters:
0:00 – Introduction and Audience Poll
0:36 – Framing Agents as Digital Employees
1:22 – NV Infobot: Architecture and Use Cases
3:00 – MAPE Framework for Self-Regulating Agents
3:34 – User Feedback Collection Challenges
5:42 – Root Cause Analysis with LLM Assistance
7:32 – Key Error Types and Prioritization
8:59 – Building the Data Flywheel
9:55 – NVIDIA Microservices for Agent Development
11:04 – Developer Workflow and Fine-Tuning
11:37 – Experiment 1: Router Optimization
13:06 – Experiment 2: Query Rephrasal Improvement
14:45 – Summary: Monitor, Analyze, Plan, Execute
15:32 – W&B Blueprint: Deploying Your Own Flywheel
16:44 – Q&A: Error Prioritization and Nemo Tools
19:30 – Closing Remarks and Applause
