christopheromar

Debugging agent traces is tough, so applying RL to error detection is a smart angle. That said, the post leaves out key details that matter to practitioners running evaluations:

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.

These are some pretty significant compliance claims, especially around ITAR, CUI, and FedRAMP High equivalency. I think the technology sounds interesting, but I'd like to understand what's actually happening behind the scenes before taking those claims at face value.

A few things I'd be curious about:

- *ITAR compliance:* What does that mean in practice for Consus? Are there specific controls around personnel access, data residency, subcontractors, and handling export-controlled information?

- *FedRAMP equivalency:* Was there an independent assessment covering all 410 FedRAMP High controls, or is this based on internal control mapping? Also, is there any publicly available documentation showing the assessment scope or authorization status?

- *Data handling:* Where do prompts and responses go? Are they stored, and if so, for how long? Can customers disable retention? I'd also be interested in how logging works and whether downstream model providers can retain data for training.

- *Customer isolation:* Are customers running in separate environments with dedicated encryption keys, or is isolation handled logically within shared infrastructure?

- *Security enforcement:* How does the system handle prompt injection or attempts to generate restricted technical information? Is there an additional security layer beyond the model itself, and can customers audit those decisions?

- *Encryption and key management:* Who manages the encryption keys, how often are they rotated, and what safeguards prevent unauthorized access?

I understand that not every security detail can be made public, but even a high-level explanation of the assessment scope, control coverage, and how data moves through the system would help.

There's definitely value in making AI accessible for regulated environments. I just think the distinction between claiming compliance and demonstrating how it's enforced matters a lot, especially for organizations dealing with sensitive information.

Would be interested to hear how the team approaches these challenges.

Thanks