Test Your Mpower Agent

This page provides information about testing an Mpower Agent A virtual agent created with CXone Mpower Agent Builder that can handle voice or chat interactions. in Agent Builder. This is the fourth step in the Mpower Agent implementation process.

Glossary

Concept	Definition	Example	What the Mpower Agent Does
Utterance	Anything a contact The person interacting with an agent, IVR, or bot in your contact center. says in an interaction The full conversation with an agent through a channel. For example, an interaction can be a voice call, email, chat, or social media conversation.. Sometimes called a message.	"I lost my password." "What is my balance?" "Are you a bot?"	The Mpower Agent uses Natural Language Understanding (NLU) to analyze each contact utterance to determine its meaning, or intent.
Intent	What the contact wants to communicate or accomplish. Every message the contact sends has an intent.	"I lost my password" has the intent of "reset password". "Hello" has the intent of "greeting".	The Mpower Agent analyzes a contact's message using NLU This process expands on Natural Language Processing (NLP) to make decisions or take action based on what it understands. to determine the intent. Once it knows that, it can respond with a message of its own. You configure the response you want your Mpower Agent to use for each intent.
Entity	A defined piece of information in a contact's message.	Person or product name, phone number, account number, location, and so on.	The Mpower Agent uses NLU to identify entities in a contact's message. Entities help the Mpower Agent understand what the contact's message means.
Slot	An entity extracted from a contact's message and saved for use in Mpower Agent responses. Similar to a variable.	Creating a slot for contact name lets the Mpower Agent use that name in responses during an interaction, making it more personal.	When configured to do so, the Mpower Agent extracts an entity from a contact message and saves it in a slot. You can have your Mpower Agent use this information later in the conversation.
Rule	Defines Mpower Agent responses to messages that don't change meaning with context.	Single-turn interactions with fixed responses: What are your hours? What is your address? Conversation building blocks: Greetings, good-byes, thank yous and transitions; simple yes/no questions; and acknowledgments. Mpower Agents come with default intents and rules for several of these, including greetings, handover The transfer of a contact from a virtual agent to a live agent. requests, and more. FAQs Insults and classic bot challenges	Rules are one of two ways you can configure how your Mpower Agent responds to an intent. Rules are useful for certain kinds of intents, but not all intents.
Story	Trains an Mpower Agent to handle an interaction based on message intent and conversational context.	In an interaction about a forgotten password, the Mpower Agent would respond to "How do I do that?" in one way. If the interaction were about creating a new account, the response would be quite different even though in both cases the contact is using the same words with the same intent—to get more information.	Stories are the second of two ways you can configure how your Mpower Agent responds to an intent. Stories teach the Mpower Agent how to use the context of the conversation to respond appropriately.
Mpower Agent Action	Anything an Mpower Agent says or does while handling an interaction.	In an interaction about a forgotten password, the Mpower Agent responds by sending the link to the password reset FAQ on the website. When a contact expresses frustration, such as "I don't understand! It's not working!!!" the Mpower Agent responds with "I'm sorry. Would you like me to transfer you to a human agent?" When the contact says yes, the Mpower Agent initiates the transfer.	Mpower Agent actions are the options you have when defining how you want your Mpower Agent to respond to each intent. They give you the flexibility to configure each response to achieve the outcome that meets the contact's needs.

Test Your Mpower Agent with Conversations

Having conversations with your Mpower Agent is the best way to test it. This allows you to observe first-hand how well it predicts intents The meaning or purpose behind what a contact says/types; what the contact wants to communicate or accomplish.. When the Mpower Agent predicts an intent incorrectly or with low confidence, you can make adjustments and immediately see the results.

You can chat with your Mpower Agent using a built-in chat window in Agent Builder. This chat window provides extra information that won't be available in production chats. You can use the information as you go through test conversations to help determine when changes are needed in the Mpower Agent response configurations.

For example, in the following image you can see that it shows the intent prediction of the test user's message and the intents assigned to each of the quick reply options in the Mpower Agent response. Under each message that the Mpower Agent sends is a drop-down that contains information about the message, including the Mpower Agent model Version of a bot that has been trained and staged being tested and the message ID. You can see the same information when viewing the conversation on the Conversations tab in Insights.

The conversations you use in testing should come from real-world interactions. By replicating those conversations with your Mpower Agent, you can test how it handles them. You can start out by using the examples you collected earlier in the implementation process. You can collect new training examples and use those, too.