Behavioral canaries
Small, fixed probe tasks run continuously against a deployed agent. Each canary has a known-good outcome; a change in that outcome is treated as an early regression signal — model drift, prompt rot, tool API changes — before users feel it. The chaos-engineering steady-state hypothesis applied to agent behavior instead of infrastructure metrics.
Why this wins its question: Transfers a proven infrastructure discipline (steady-state probing) to agent behavior with the operational details — pinned outcomes, schedule, drift-as-alarm — from a team that runs it, not from analogy alone.
Claims
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A behavioral canary is a probe task with a pinned expected outcome, executed on a schedule against the production agent; outcome drift is an alarm, and the practice is part of the production methodology behind this instance.
Chaos engineering grounds the pattern: define steady state as measurable output that indicates normal behavior, then treat deviations from that baseline as the signal worth alarming on.