Best Practices for CXone Mpower Agent Builder

This page provides the recommended best practices for using Agent Builder.

Naming and Creating Your Bot

• Don't use a real agent name for your Mpower Agent. If you use a human-sounding name, choose one that's unlikely to belong to a real person.
• Don't use the same employee profile for more than one Mpower Agent. Each Mpower Agent should have its own unique profile for routing and reporting purposes.
• Do use the same name for the Mpower Agent and its employee profile. This keeps things simple when you're managing multiple Mpower Agents.
• Do use an email address you have access to. This allows you to receive and respond to the invitation to CXone Mpower. You can use the same email address for multiple Mpower Agent employee profiles.

Writing for Your Mpower Agent

• Decide the personality of your Mpower Agent and its vocabulary ahead of time. Remember that your Mpower Agent is a face of your organization, just as human agents are. Ensure that its manner gives the right impression. Be clear on the mannerisms you want to replicate in it's responses.
• Write all dialogue ahead of time. This allows you to ensure that the way your Mpower Agent speaks is consistent throughout the conversation. When working on a new use-case, review the dialogue you've already written to maintain the persona across all use cases.
• Know the audience you're writing for. The language and terminology you use for the general public may differ from the language you would use for a specialized audience.
• Keep messages from the Mpower Agent short. Many people don't like to read long blocks of text. The more text your Mpower Agent sends, the less engaging it's likely to be. If there's a lot of information to send, consider breaking it into several shorter responses.
• Use the variations option when you add messages to a story. This allows you to add additional versions of the same message. The Mpower Agent uses one of the versions randomly each time it uses that response. Variations makes your Mpower Agent seem more human-like and improves the contact's The person interacting with an agent, IVR, or bot in your contact center. experience while interacting with the Mpower Agent.
• Use Smart Typing to display typing indicator dots for the contact. This creates a positive user experience. An Mpower Agent that replies instantly feels too "bot-like" and many people don't like that. After you enable Smart Typing, you can customize the length of time the Mpower Agent displays the typing indicator for every message it sends.
• Read through the conversation out loud a few times. You might consider role-playing the conversation with someone else. You could also record yourself reading the responses scripted for your Mpower Agent, then listen to it. These are all good ways to spot places in the scripted responses that need improvement.

Intents

• Intents aren’t always clear-cut. Two user goals might seem different at first but start to gather similar examples over time. Keep your intents and their training data distinct. If you want to reuse training examples for more than one intent, it's a sign that you might be able to merge the intents into a single, more general intent instead.  This helps you avoid intent confusion.
• Always include an out-of-scope intent. Out-of-scope intents allow your Mpower Agent to respond to contact requests that are outside the tasks it is trained to do.They allow you to recover the conversation and often result in improved performance.
• Test and train. Test your Mpower Agent to uncover problem intents. For example it might provide incorrect responses or choose an out of scope intent at times when you think it should have chosen a different intent.
• Use multi-intents sparingly. Only use multi-intents when they are really necessary to the natural flow of conversation. Too many multi-intents can make your Mpower Agent too complicated to manage easily.

Rich Messaging

• Verify channel support. Not all rich media Elements in digital messaging such as buttons, images, menus, and option pickers. types are supported by all digital channels Various voice and digital communication mediums that facilitate customer interactions in a contact center.. You can check the current matrix of support.
• Use rich messaging fallback. This type of fallback allows you to provide a backup for channels that don't support a rich messaging option you use in an Mpower Agent response.
• Know the file type and size limitations. Mpower Agents support a variety of multimedia types, including audio and video. There are limitations on the size and supported file types for all multimedia.

Rules

• Don't overuse rules. Mpower Agents cannot use them to generalize unforeseen conversation paths. They should only be used for small, specific conversation patterns.
• Only use rules when the response is always the same. If there's a chance that some contexts may require a different answer, use a story Used to train an Mpower Agent for interaction handling based on intent and context. instead.
• Don't use rules if you want variation in response. Even if a rule is an appropriate tool for a particular message, you might want your Mpower Agent to vary its responses so it sounds more human. If this is the case, use stories instead.
• Use conditions with a rule if you want to teach your Mpower Agent when to apply the rule. Conditions can be set based on the active form, a specific slot, or a specific slot value.

Stories

• Use stories when context is important. If your Mpower Agent needs context to understand how to respond, use a story. This is true even if a conversation only involves one exchange between your Mpower Agent and the contact The person interacting with an agent, IVR, or bot in your contact center.. For example, if you have a lookup_balance intent, but some contacts want the balance of a checking account and others want to know about a savings account, you could create a story to help your Mpower Agent learn to respond appropriately based on which account a user specifies.
• Use stories to help your Mpower Agent learn to make predictions. Choose the subject of each story carefully. Ensure that it's designed to help the Mpower Agent learn to correctly predict responses for conversations it hasn't seen before.
• Base stories on real-world conversations. Don't make up stories that you think might happen. Use real interactions to create them instead.

• Design stories that follow either a happy Story that produces the correct outcome for the intent. path or an unhappy path Story that produces a wrong outcome for the intent.. Combining paths in a story can lead to intent confusion.

• Use stories to handle context-switching. This helps your Mpower Agent learn to switch between two conversation flows or handle interruptions that take more than one conversation turn to respond to. If an interruption only takes a single turn to respond to and doesn't depend on context, a rule may be more appropriate.

• Some intents need multiple stories. Create multiple stories for the same intent if there are variations of how the conversation might go, based on the contact's unique situation and needs.

  • Don't include variations of the conversation flow in the same story. This could confuse the Mpower Agent.
  • If there are variations to the way a contact might phrase a message, or similar messages that all mean essentially the same thing, you can add them as examples of the intent for a contact message.

  Think in terms of happy and unhappy paths. Each intent can have more than one happy path and more than one unhappy path.

• Create a story for your out-of-scope intent. This allows you to train your Mpower Agent on the more common ways that contacts present out-of-scope information.
• Include back-and-forth between the contact as needed, but carefully. Stories and rules should not be complete conversations. When the next statement in the conversation would necessarily start a new intent, it's time to stop and create a new story.
• Break your stories into logical subtasks. It's tempting to create one long story that encompasses the entire conversation from start to finish. However, this can actually increase the number of stories you need. Instead, break your stories into logical subtasks. If you have some subtasks that are very closely related, you can link them with checkpoints.
• Do not overuse checkpoints. They can simplify your training data. Too many checkpoints make your stories hard to understand and actually slow down the process of training your Mpower Agent.

Training Data and Examples

• Always opt for quality over quantity. It's okay to start with a small data set and build it over time as you gather more high-quality examples.
• Use examples from real-world conversations. This ensures that the data you use is realistic. It comes from utterances What a contact says or types. real contacts have made.
• Don't use tools that auto-generate data and claim to train your Mpower Agent faster. They often produce examples that don't reflect what contacts really say. They also can result in an Mpower Agent that loses its ability to generalize. Over time, the Mpower Agent reaches a point where it only recognizes phrases it's seen before.
• Do not use the same training data for more than one intent. If you re-use training data, your Mpower Agent won't be able to reliably determine the intent The meaning or purpose behind what a contact says/types; what the contact wants to communicate or accomplish. in live interactions with contacts.
• Remain flexible and willing to adjust intents and stories over time. As you review conversation data, you may discover that what you thought was two separate intents are really shades of a more general intent. Or you may find that an intent is too broad and you need to break it down into more specific intents.
• Add new training examples only if they will help.
  • Do not add new training examples that are very similar to existing examples. If the Mpower Agent correctly predicts the intent with a high confidence for one utterance, it doesn't help the Mpower Agent to add more examples that are very similar.
  • Do add more training examples of utterances the Mpower Agent has previously predicted incorrectly or with low confidence.