Improving Agents is a Data Mining Problem
Improving agents involves leveraging trace data for signal extraction, fine-tuning, and continual learning to enhance performance, while addressing the challenges of processing vast amounts of data. Open models and specialized agents are critical for efficiently handling large-scale trace data, enabling data mining and evaluation to refine agent behavior. The talk emphasizes the importance of observability, data curation, and tooling to support long-term agent improvement, with a focus on balancing rapid iteration with effective training.
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