Test-Time Compute Explained - Benchmarking and Optimizing AI Agents

Learn how to benchmark and optimize AI agents using test-time compute scaling techniques through NVIDIA's NeMo Agent Toolkit in this comprehensive tutorial. Discover how agents can enhance performance by sampling tools, testing multiple trajectories, and re-running functions to improve workflow outcomes. Explore the open-source NeMo Agent Toolkit, a framework-agnostic library that provides profiling, evaluation, and optimization capabilities for connected AI agent systems. Master the step-by-step process of implementing the test-time compute module, including searching, editing, scoring, and selection strategies. Understand the fundamentals of test-time compute and get an overview of the test-time compute module's architecture and capabilities. Learn to write custom scoring, searching, editing, and selection strategies tailored to your specific use cases. Discover how to make your custom strategies discoverable within the NeMo Agent Toolkit ecosystem for seamless integration. Follow along with a practical RAG (Retrieval-Augmented Generation) example that demonstrates real-world application of these concepts. Explore the prebuilt test-time compute functions available in the toolkit to accelerate your development process. Gain hands-on experience with configuring existing LLMs and tools to leverage these advanced optimization techniques for improved AI agent performance.