There is a system called ASA Research Observatory that is trying to observe a class of failure most monitoring systems encounter only indirectly.
It doesn't begin with the moment something breaks.
It begins with the trajectory that got there.
That distinction is worth examining because many failures don't initially present as failures. A system can continue producing valid-looking outputs while some relationship that previously made those outputs trustworthy is changing. Dependencies still resolve. Responses remain fluent. Individual operations succeed.
Something has moved, but nothing has complained yet.
Ordinary monitoring isn't necessarily blind to this. We already measure trends, rates of change, error budgets, latency distributions, resource pressure and other signals that can deteriorate before an incident becomes visible to a user. Security monitoring likewise looks for anomalous behavior before compromise produces an obvious outage.
ASA is making a more specific claim.
In long-running human-AI interaction, changes in trajectory may themselves contain information about approaching instability. Instead of waiting for a contradiction or obvious behavioral failure, the system attempts to observe whether the interaction is moving away from previously established conditions while the individual outputs can still appear locally reasonable.
That's interesting because fluency is a poor failure detector for a language model.
A response can be grammatically clean, contextually appropriate and individually defensible while participating in a longer trajectory that has gradually changed character. If we inspect only the latest output, we may have no reason to object.
The question becomes whether the change across outputs contains a detectable signal that the outputs themselves don't.
ASA gives us a way to ask that experimentally.
If its measurements begin changing before independently identified failures or loss of recoverability, then trajectory observability is contributing information we didn't already have from ordinary output inspection. If its signals move without reliably predicting anything consequential, then we may simply have created a more elaborate description of variation.
The distinction has somewhere to fail.
That matters because words such as coherence and drift can become dangerously accommodating. Once we know that an interaction eventually went wrong, it's easy to look backward and find a trajectory that appears to have been heading there all along.
A useful observability layer has to work in the other direction.
It has to tell us something before we know the ending.
That means the interesting tests aren't examples where drift and failure have already been identified. They're cases where ASA reports changing conditions and we then observe what happens next. Some trajectories should recover without intervention. Some should continue changing without producing meaningful failure. Others may cross thresholds after which recovery becomes harder or the behavior changes abruptly.
If those classes can be distinguished prospectively, we have something much stronger than a metaphor about coherence.
We have an instrument.
Seeing isn't the same as acting
There is another distinction in the architecture that I initially thought was a missing piece.
Observing an unstable trajectory doesn't tell us what should happen because of it.
A warning can be ignored. A threshold can be overridden. A human operator can decide that an unusual trajectory is justified by unusual circumstances. An automated system can continue producing outputs after an observer has correctly identified increasing risk.
That isn't necessarily a defect in observability.
It may be a boundary.
Kusowski's broader architecture appears to recognize that distinction explicitly. ASA is positioned as an external observation layer, while the related ASC — Asymmetric Stability Crystallizer is described as the response and stabilization layer