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…
Overview
This article discusses the process of scaling LangGraph agents in production, specifically focusing on the deployment of an AI-Q research agent. It outlines the tools and techniques used from the NVIDIA NeMo Agent Toolkit to ensure the agent can handle increased user loads effectively.
What You'll Learn
1
How to profile and optimize an AI agent application for a single user
2
How to conduct load testing to estimate architecture needs for multiple users
3
How to monitor application performance during a phased rollout
Prerequisites & Requirements
- Understanding of AI agent applications and their deployment
- Familiarity with the NVIDIA NeMo Agent Toolkit(optional)
Key Questions Answered
How can I scale my LangGraph agents to support multiple users?
To scale LangGraph agents, you should profile the application for a single user to identify bottlenecks, conduct load testing to estimate architecture needs for multiple users, and monitor performance during a phased rollout. This process ensures that the agent can handle increased user loads effectively.
What tools can help in profiling and optimizing AI applications?
The NVIDIA NeMo Agent Toolkit provides evaluation and profiling tools that help gather data on application performance. It allows developers to track timing, token usage, and other metrics to identify bottlenecks and optimize the application for better performance.
What are the steps to estimate hardware needs for scaling an AI agent?
To estimate hardware needs, run load tests at varying concurrency levels to collect data on performance metrics. This data can then be used to forecast the number of GPUs required to support the desired number of concurrent users while maintaining acceptable latency.
Key Statistics & Figures
Concurrent users supported per GPU
10
One GPU can support 10 concurrent users within the latency threshold.
Technologies & Tools
Tool
Nvidia Nemo Agent Toolkit
Used for profiling, optimizing, and monitoring AI agent applications.
Application
Ai-q Nvidia Blueprint
Framework for building and deploying AI agents.
Platform
Openshift
Used for deploying the internal architecture of the AI-Q research agent.
Key Actionable Insights
1Profile your AI agent application to identify performance bottlenecks before scaling.Understanding how your application performs under single-user conditions helps in making informed decisions about scaling and resource allocation.
2Conduct thorough load testing to gather data on how your application handles multiple users.This data is crucial for forecasting hardware needs and ensuring that the application can maintain performance under load.
3Use monitoring tools like the NeMo Agent Toolkit OpenTelemetry collector to track application performance during rollout.Monitoring allows for real-time adjustments and improvements, ensuring a smoother user experience as more users access the application.
Common Pitfalls
1
Failing to identify bottlenecks during the profiling stage can lead to performance issues when scaling.
Without understanding how the application behaves under single-user conditions, scaling efforts may exacerbate existing issues.
2
Neglecting to monitor application performance during rollout can result in undetected failures.
Monitoring is essential to catch issues early and ensure that the application can handle increased loads without degrading user experience.
Related Concepts
AI Agent Applications
Load Testing Methodologies
Performance Optimization Techniques