An AI agent often loops during incident alert scheduling primarily due to conflicting or ambiguous rules within its operational parameters. For instance, if presented with mutually exclusive scheduling constraints, such as "send alert immediately" and "delay alert by one hour for this specific incident type," the agent might repeatedly attempt to resolve the contradiction without a clear path forward. This behavior can also stem from a poorly defined state machine, where the agent fails to correctly update its internal state after each scheduling attempt, causing it to revert to a previous configuration and re-evaluate the same conditions. Furthermore, a missing or insufficient termination condition in its core algorithm means the agent won't recognize when it's stuck in an infinite retry cycle, continuously trying to achieve an impossible state. Lastly, dependencies on unresponsive or slow external systems, coupled with an aggressive retry policy without adaptive back-off, can trap the agent in a loop while awaiting resolution from an external dependency. More details: https://www.nathaliewinkler.com/showreel.php?parent=63&link=infoguide.com.ua