Stream integrity for artificial intelligence conversations

A system having one or more computer processors, the system configured to:

• monitor an artificial intelligence enabled communication;
• analyze messages, participants, and actions within the communication;
• identify critical data within the communication, wherein the critical data is data that is important for performing the actions within the communication;
• determine critical sequence data, wherein the critical sequence data is a sequence of conversation elements that is within the communication and that contains the critical data;
• predict an uncertainty level for the critical sequence data, wherein the uncertainty level indicates an amount of uncertainty that the artificial intelligence correctly interprets the critical sequence data;
• determine that the uncertainty level for the critical sequence data is above a threshold uncertainty value, resulting in critical sequence data that is subject to misinterpretation;
• and
• in response to determining that the critical sequence data is subject to misinterpretation, resolve any uncertainties for the critical sequence data.

A computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a server to cause the server to perform a method, the method comprising:

• monitoring an artificial intelligence enabled communication;
• analyzing messages, participants, and actions within the communication;
• identifying critical data within the communication, wherein the critical data is data that is important for performing the actions within the communication;
• determining critical sequence data, wherein the critical sequence data is a sequence of conversation elements that is within the communication and that contains the critical data;
• predicting an uncertainty level for the critical sequence data, wherein the uncertainty level indicates an amount of uncertainty that the artificial intelligence correctly interprets the critical sequence data;
• determining that the uncertainty level for the critical sequence data is above a threshold uncertainty value, resulting in critical sequence data that is subject to misinterpretation;
• and
• in response to determining that the critical sequence data is subject to misinterpretation, resolving any uncertainties for the critical sequence data.