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8h ago · 9 min read · When an LLM agent fails, the trace is right there, the user turns, the tool calls, the responses, the final result. But knowing what happened and knowing why it failed are two different things. Most t
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1d ago · 12 min read · Fine-tuning a model requires data. Good data requires human labeling. Human labeling doesn't scale. And most synthetic generation pipelines stop at generation, they produce candidate pairs but have no
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2d ago · 5 min read · A 10-turn agent session can easily accumulate 20,000+ tokens of raw history, leaving almost no room for the current task. Naive truncation drops older turns wholesale, including the decisions and disc
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