Machine Learning, the long-awaited functionality of the Now platform, has come out with the Kingston release. The new application in ServiceNow providing machine learning features is called Agent Intelligence. The application is now available on personal development instances; so to give ServiceNow experts and customers interested in the topic an insight, in this blog post we are going to take a look at setting up and using Agent Intelligence features.
Agent Intelligence helps companies drive automation by using a machine learning solution that can learn and improve. The solution creates machine learning models that automatically populate selected fields of records; for example, they can categorise and assign incidents based on the short description with a continuously improving efficiency.
Agent intelligence is a customer-specific solution, which gives predictions based on each customer’s own set of data that is already available in ServiceNow, such as incidents, requests, problems, changes, or practically any other records.
The solution model behind Agent Intelligence is built on three elements:
(picture source: ServiceNow Docs Site)
When, for example, a new incident record is submitted, a before insert business rule in the background calls the MLPredictor Script Include, which checks the available solution models for a prediction and then populates target fields automatically when a record is submitted.
The solution model’s inputs are the records that are already in the ServiceNow instance. The input fields of the model are used to make predictions; for example, the incident’s short description can be used to make a prediction for the assignment group, as an output field.
Agent Intelligence can be extended to any process in ServiceNow by creating custom solution models and training those models on existing customer data.
The activation of the Agent Intelligence plugin can be requested from ServiceNow’s HI customer system.
After activating the new plugin, a solution definition should be set up, that means, filters, inputs and outputs need to be defined. Filters will determine the set of data that will be used for training a predictive solution model. Inputs are the fields that will then be used as an input of a prediction request from a new record, and finally the output field is predicted based on the input fields and a related, pre-trained solution model.
After setting up a solution definition, it needs to be trained and the resulting solution model needs to be tested to see if the accuracy and coverage of the solution are good enough. After that, the solution model is ready to be used immediately.
The usage of Agent Intelligence is automatic for the records determined in the solution definitions, so agents who work with Agent Intelligence only need to be aware that certain fields are expected to be predicted automatically.
Trained solution models have a version, meaning that solution definitions can be retrained any number of times, and only the latest solution model will be active, so the system does automatic versioning for the trained solution models.
Reports can be reviewed periodically, and if needed solution definitions, solution model precision and coverage levels can be adjusted continuously to fit expectations.