How the AI Loop Helps You Innovate Faster | CoreWeave Blog

This post introduces the 'AI Loop' concept, detailing a framework for continuous improvement of AI models and agents. It describes the integration of CoreWeave's inference and training platforms with Weights & Biases tools (Weave traces, Registry, Evaluations, Models, Automations) to create a feedback loop. Key components include running models/agents, observing their behavior, curating failures into datasets, evaluating improvements, and iterating. It highlights the distinction between agentic loops (prompt/skill improvements) and deeper model training loops (fine-tuning, RL on production data). The post also introduces CoreWeave ARIA as an AI Research and Iteration Agent that orchestrates this loop by analyzing runs, forming hypotheses, and launching experiments. It emphasizes the importance of observability across both the AI application and underlying infrastructure.