Metrics are green.
Behavior has drifted.
Intent-aware monitoring for AI systems.
We detect semantic drift: when your system stops behaving as designed, even if nothing looks broken.
Current tools ask
- Did it crash?
- Did latency spike?
- Did error rates increase?
We ask
- Is it still doing what it was designed to do?
- Has behavior shifted in ways metrics don't capture?
- Are agents optimizing toward unintended outcomes?
How it works
Long-running agents that observe, reason, and report.
01
Learn normal
Behavioral baselining across releases, workflows, and edge cases. No manual threshold tuning.
02
Understand intent
Ingests product intent from PRs, specs, runbooks, and policies. In natural language.
03
Detect divergence
Surfaces when real-world behavior diverges from declared intent. Before users notice.
Software used to be correct or broken.
Now it can be aligned or adrift.
The infrastructure layer for systems that can't be fully specified upfront.
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