You’ve built a powerful AI agent and are ready to share it with your colleagues, but have one big fear: Will the agent work if 10, 100, or even 1,000…

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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.

How to Scale Your LangGraph Agents in Production From A Single User to 1,000 Coworkers

How to build a secure, scalable deep-researcher

Step 1: How do you profile and optimize a single agentic application?

Step 2: Can your architecture handle 200 users? Estimating your needs

Step 3: How to monitor, trace, and optimize your research agent’s performance as you scale up to production