Graphical user interface and alerting system for detecting and mitigating problems in an agent network

A method comprising:

• receiving, by one or more processors of a computing system of an entity, a plurality of sets of usage data from a plurality of agent computer systems associated with a plurality of agents, each agent computing system of the plurality of agent computing systems being associated with a corresponding agent of the plurality of agents and providing a corresponding set of usage data of the plurality of sets of usage data, wherein:
• the plurality of agent computing systems are different from the computing system,
• the plurality of agents are different from the entity,
• the plurality of agent computing systems provide an infrastructure to support a plurality of service providers in providing one or more services to a plurality of service users,
• the plurality of service providers are distinct from the plurality of agents, the entity, and the plurality of service users, and
• the plurality of sets of usage data describe interactions between the plurality of service providers and the plurality of service users;
• generating, by the one or more processors, a corresponding set of metric values for a common set of metrics for each respective agent of the plurality of agents based on a corresponding set of usage data of the plurality of sets of usage data, wherein:
• at least one metric value of the corresponding set of metric values is generated based on an output from a trained machine-learning model,
• the common set of metrics are usable for all of the plurality of agents to detect anomalies related to the plurality of agents, and
• the corresponding set of metric values for at least one agent of the plurality of agents is generated at least in part by normalizing the corresponding set of usage data from a first format to a second format;
• generating, by the one or more processors, a respective score for each respective agent of the plurality of agents based on the corresponding set of metric values, wherein the respective score indicates a respective risk level associated with the respective agent;
• comparing, by the one or more processors, the respective score for each respective agent of the plurality of agents to a predefined threshold to identify one or more agents among the plurality of agents having respective scores that meet or exceed the predefined threshold;
• and
• generating, by the one or more processors, a graphical user interface indicating the one or more identified agents.

A non-transitory computer-readable medium including program code that is executable by one or more processors of a computing system of an entity for causing the one or more processors to:

• receive a plurality of sets of usage data from a plurality of agent computer systems associated with a plurality of agents, each agent computing system of the plurality of agent computing systems being associated with a corresponding agent of the plurality of agents and providing a corresponding set of usage data of the plurality of sets of usage data, wherein:
• the plurality of agent computing systems are different from the computing system,
• the plurality of agents are different from the entity,
• the plurality of agent computing systems provide an infrastructure to support a plurality of service providers in providing one or more services to a plurality of service users,
• the plurality of service providers are distinct from the plurality of agents and the plurality of service users, and
• the plurality of sets of usage data describe interactions between the plurality of service providers and the plurality of service users;
• generate a corresponding set of metric values for a common set of metrics for each respective agent of the plurality of agents based on a corresponding set of usage data of the plurality of sets of usage data, wherein:
• at least one metric value of the corresponding set of metric values is generated based on an output from a trained machine-learning model,
• the common set of metrics are usable for all of the plurality of agents to detect anomalies related to the plurality of agents, and
• the corresponding set of metric values for at least one agent of the plurality of agents is generated at least in part by normalizing the corresponding set of usage data from a first format to a second format;
• generate a respective score for each respective agent of the plurality of agents based on the corresponding set of metric values, wherein the respective score indicates a respective risk level associated with the respective agent;
• compare the respective score for each respective agent of the plurality of agents to a predefined threshold to identify one or more agents among the plurality of agents having respective scores that meet or exceed the predefined threshold;
• and
• generate a graphical user interface indicating the one or more identified agents.