Safe CI gating for systems that aren’t perfectly repeatable.
Modern systems are not deterministic.
Robots, agents, simulations, and real-world workflows all exhibit noise, variance, and timing drift. Saykai is built for that reality.
Saykai enforces safety gates based on meaningful behavioral change, not one-off variance or flaky execution.
Saykai does not require bit-for-bit identical replays. It evaluates whether system behavior has changed outside an accepted baseline.
This allows teams to enforce safety in CI without introducing instability.
Saykai compares runs against behavioral envelopes, not single frozen traces. Baselines can include acceptable outcome ranges, invariant conditions, and tolerance bounds defined by the team.
This preserves natural variation while still detecting regressions.
Behavior is evaluated across multiple runs, not against a single expected output.
For MVP and early pilots:
This dramatically reduces false positives while preserving signal.
Every Safety Pack records:
Nondeterminism is explicit and reviewable, not buried in logs.
Teams can run Saykai in:
This allows trust to build before enforcement becomes mandatory.
No safety system should silently halt progress. Saykai supports:
Overrides preserve accountability without bypassing safety.
Saykai does not claim:
Saykai provides structured evidence and enforcement, not blind automation.
This approach is built for:
If your system must change safely, nondeterminism must be handled explicitly.
That’s the standard required for real safety gates in CI.
Run Saykai against your own scenarios.
See how behavioral gating works in your CI pipeline.
Request Pilot Access