Learning from what users fix: turning agent mistakes into training data

Raw interaction logs between AI agents and users are noisy and inefficient for training. Echo captures the high-quality signal hidden in user refinements—when people fix flawed AI outputs—and feeds those corrections back into model training. Tested on a production code completion system, this approach broke performance plateaus by learning directly from what users actually need, rather than relying on static curated datasets.