NVIDIA shares their approach to scaling LangGraph AI agents from single-user prototypes to production systems supporting 1,000+ concurrent users. The process involves three key steps: profiling single-user performance to identify bottlenecks, conducting load tests to estimate hardware requirements, and implementing monitoring during phased rollouts. Using the NeMo Agent Toolkit, they deployed an internal AI-Q research agent, discovering critical issues like CPU misconfiguration and timeout handling that only emerged under load. The methodology includes evaluation tools, sizing calculators, and OpenTelemetry integration for comprehensive observability.
Table of contents
How to build a secure, scalable deep-researcherStep 1: How do you profile and optimize a single agentic application?Step 2: Can your architecture handle 200 users? Estimating your needsStep 3: How to monitor, trace, and optimize your research agent’s performance as you scale up to productionConclusionSort: