Abstract: An automated agent may communicate with a user via a chat channel to proactively alert the user of an L1 IT support issue. The L1 IT support issue may be determined based on monitoring indications of human-initiated activities maintained by a system of record, and may, prior to the automated agent's alert, be unknown to the user. In some instances, a natural language understanding (NLU) module may be used to identify an entity and intent from the indications of human-initiated activities, and the L1 IT support issue may be determined based on the determined entity and intent. After alerting the user of the L1 IT support issue, the automated agent may inform, via the chat channel, the user of a remediation step available to address the L1 IT support issue. Upon obtaining the user's permission, the automated agent may perform the remediation step to address the L1 IT support issue.

Abstract: An automated agent may communicate with a user via a chat channel to proactively alert the user of an L1 IT support issue. The L1 IT support issue may be determined based on monitoring indications of human-initiated activities maintained by a system of record, and may, prior to the automated agent's alert, be unknown to the user. In some instances, a natural language understanding (NLU) module may be used to identify an entity and intent from the indications of human-initiated activities, and the L1 IT support issue may be determined based on the determined entity and intent. After alerting the user of the Ll IT support issue, the automated agent may inform, via the chat channel, the user of a remediation step available to address the L1 IT support issue. Upon obtaining the user's permission, the automated agent may perform the remediation step to address the L1 IT support issue.

Abstract: An automated agent may communicate with a user via a chat channel to proactively alert the user of an L1 IT support issue. The L1 IT support issue may be determined based on monitoring indications of human-initiated activities maintained by a system of record, and may, prior to the automated agent's alert, be unknown to the user. In some instances, a natural language understanding (NLU) module may be used to identify an entity and intent from the indications of human-initiated activities, and the L1 IT support issue may be determined based on the determined entity and intent. After alerting the user of the L1 IT support issue, the automated agent may inform, via the chat channel, the user of a remediation step available to address the L1 IT support issue. Upon obtaining the user's permission, the automated agent may perform the remediation step to address the L1 IT support issue.

Abstract: A form filling application is configured to minimize the form filling effort of a user. The configuration follows concepts from reinforcement learning, and includes optimizing a policy for selecting agent actions in a manner that maximizes a reward signal. In the context of the form filling application, an agent action may specify one or more slots of the form for the user to fill, and further specify one or more user interfaces for filling the specified one or more slots. The reward signal may be defined as an inverse function of the user effort, so that maximizing the reward signal has the desired effect of minimizing the user effort.

Abstract: An automated agent may communicate with a user via a chat channel to proactively alert the user of an L1 IT support issue. The L1 IT support issue may be determined based on monitoring indications of human-initiated activities maintained by a system of record, and may, prior to the automated agent's alert, be unknown to the user. In some instances, a natural language understanding (NLU) module may be used to identify an entity and intent from the indications of human-initiated activities, and the L1 IT support issue may be determined based on the determined entity and intent. After alerting the user of the L1 IT support issue, the automated agent may inform, via the chat channel, the user of a remediation step available to address the L1 IT support issue. Upon obtaining the user's permission, the automated agent may perform the remediation step to address the L1 IT support issue.

Abstract: A form filling application is configured to minimize the form filling effort of a user. The configuration follows concepts from reinforcement learning, and includes optimizing a policy for selecting agent actions in a manner that maximizes a reward signal. In the context of the form filling application, an agent action may specify one or more slots of the form for the user to fill, and further specify one or more user interfaces for filling the specified one or more slots. The reward signal may be defined as an inverse function of the user effort, so that maximizing the reward signal has the desired effect of minimizing the user effort.