1. What exactly counts as an error in your benchmark? Is it binary error vs. no error, or do you pinpoint root causes like tool failures or policy violations? How do you get ground truth—human labels, heuristics, or replayed failures?
2. What metrics beyond one detection score do you use? Precision, recall, false positive rate, and model calibration all impact real-world debugging value. Any measure of whether this actually speeds up fixes would be helpful too.
3. The “23x cheaper” claim needs context. What are typical trace lengths, token counts, decoding settings, latency, and throughput? Were the models tested under comparable conditions?
4. What failure modes have you observed? Do false positives occur when the model misattributes causes? Does your approach generalize well beyond your dataset, or does it rely on brittle triggers?
5. Finally, can you share datasets, trace formats, and evaluation scripts? Without these, it’s hard for others to verify or build on your results.
Addressing these points would make this more than a proof of concept—turning it into a practical, reproducible foundation for agent observability rather than just another hype cycle.