A Powerful Data Flywheel for De-Risking Agentic AI

NVIDIA NeMo microservices, part of NVIDIA AI Enterprise, and the Galileo platform create a powerful toolchain enabling developers to achieve high accuracy and reliability in agentic AI systems. An AI data flywheel is a systematic process that creates a virtuous cycle of continuous improvement for AI systems. The toolchain uses an end-to-end platform for building data flywheels, allowing enterprises to develop and continuously optimize their AI agents with the latest information. Evaluating models and AI workflows is crucial in building agentic applications, as it enables developers to measure the performance of their AI applications and provide powerful insights on which trajectories are unexpected in an agent's execution. The Galileo platform implements a five-stage process for implementing the AI data flywheel, including data curation, model customization, evaluation, guardrails, and deployment and observability. By using this approach, organizations can transform agentic system development into a systematic engineering practice for iterative improvement and achieve higher tool selection accuracy and faster detection latency in production environments.

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