Everyone deserves great experiences at work. Now routine tasks happen in a snap, and complex projects flow smoothly. With the Now Platform® , anyone can work effortlessly across teams, silos, and systems. When people work better, business works better.
ServiceNow’s Agent Intelligence is a new feature in the latest Kingston Release, it uses machine-learning algorithms to set field values during record creation such as setting the incident category based on the short description. Train predictive models to act as an agent to categorize and route work based on your past record-handling experience. Today we are going to go in-depth on how this actually works, so we will discuss about:
Benefits of Agent Intelligence
Predictive Models to use
Record Selection for training
Benefits of Agent Intelligence
Enable Agent Intelligence to handle higher volumes of incoming requests at lower costs. Automate the categorization and assignment of requests to gain these benefits.
Reduce task resolution times.
Reduce the number of interactions required to resolve tasks.
Reduce the error rates of categorizing and assigning work.
A predictive model consists of these elements.
A solution definition is a configuration record that specifies how to train a predictive model. All solution definitions specify these values.
The records used to train the model. For example, only train on incidents that have been resolved or closed within the last 6 months.
The input fields the model uses to make predictions. For example, use the incident short description to make a prediction.
The output field whose value the model predicts. For example, set the incident category based on the short description.
The frequency to retrain the model. For example, retrain the model every 30 days.
A solution is a Java object produced by training a solution definition. Agent Intelligence uses this object to predict a target field value given one or more input field values. All solutions specify these values.
The solution precision is the aggregate percentage of correct predictions. For example, a precision of 50 means that out of 100 predictions, half of them should have the correct value.
The solution coverage is the aggregate percentage of records that receive a prediction. For example, a coverage of 50 means half of all eligible records actually receive a prediction.
The solution classes are the output field values for which the model can make predictions. Each class is an output field value with a list of possible precision, coverage, and distribution metrics to choose from. For example, the Incident Categorization solution has a class for each category such as software, inquiry, and database.
The class distribution is the percentage of records from the entire table that have this particular output field value. For example, a distribution of 50 for the inquiry class means that half of incidents have the inquiry category.
A business rule that calls the solution data set to generate a prediction when a new record is created.
By default, the system offers these predictive model solutions.
Incident categorization: Predicts the incident category based on the short description.
Incident prioritization: Predicts the incident priority based on the short description.
Task assignment: Predicts the task record assignment group based on the short description.
You can extend Agent Intelligence to other processes by creating your own predictive models and training them on your past record data.
A solution is only as good as the record data you use to train it. In general, a good training dataset has these characteristics.
The solution definition input fields are available to users when creating records. To make predictions at record creation, the solution must have the input field values at record creation.
The solution definition output field is a choice field. To make more accurate predictions, limit the output field to a finite set of possible values.
The training records only contain correct values for the output field. To make more accurate predictions, filter out any records that have unreliable output field values. For example, if recently closed incidents are subject to review and change for a month, filter out any recently-closed incidents.
The training records contain multiple examples of each output field value you want the solution to predict. To provide more record coverage, include multiple examples of each output field value.
The training records include common variations of the input fields. To provide more record coverage, include multiple examples of input field values.
Training a solution involves exporting the solution definition and its associated records to the nearest training service at a ServiceNow datacenter. Training services are not available to customers who have data sovereignty requirements. When training is complete, the training service exports the solution back to the instance and deletes all customer training data from the training service servers.
Note: All communications between the instance and the training service are over HTTPS.