Intent-based chaos testing is designed for when AI behaves confidently — and wrongly
Here is a scenario that should concern every enterprise architect shipping autonomous AI systems right now: An observability agent is running in production. Its job is to detect infrastructure anomalies and trigger the appropriate response. Late one night, it flags an elevated anomaly score across a production cluster, 0.87, above its defined threshold of 0.75. The agent is within its permission boundaries. It has access to the rollback service. So it uses it.
The rollback causes a four-hour outage. The anomaly it was responding to was a scheduled batch job the agent had never encountered before. There was no actual fault. The agent did not escalate. It did not ask. It acted, confidently, autonomously, and catastrophically.
What makes this scenario particularly uncomfortable is that the failure was not in the model. The model behaved exactly as trained. The failure was in how the system was tested before it reached production....
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