The Monitoring Platform

Agents start in the right mode, then drift into unsafe, off-brand, or confusing behavior without any visible error.

A way to watch whether an agent still looks like the assistant you meant to ship, and flag the moment it starts slipping out of character.

Systems get pulled off objective by prompt injection, conflicting instructions, or long-context confusion — and surface-level outputs look fine.

A way to catch when the model stops following the job you gave it, even if the output still sounds polished.

Output sounds confident, but the organization needs a better signal for weak grounding or likely hallucination when it shows up in a live workflow.

A way to spot when a model sounds more certain than it really is, so risky content can be reviewed with a clearer understanding of what looks shaky.

Teams need runtime control over model generation — constrained outputs, forced continuations, logit reweighting — without retraining.

A shipped control surface for changing generation behavior at runtime when monitoring is not enough and you need to intervene directly.