Objective Ordering in AGENT AI Chat Bot

In building AI chatbots, the sequence in which tasks are performed can significantly impact user experience. AGENT AI Chat Bot features a robust objective ordering system that ensures conversations flow logically and efficiently. In this blog post, we'll explore how objective ordering works and how it enhances your bot's performance.

Understanding Objective Ordering: Objective ordering refers to the prioritisation of tasks or objectives that your chatbot needs to accomplish during an interaction. These objectives are arranged in priority levels, and the bot follows these levels to determine the sequence of actions.

Example Scenario: Consider an example with the following objectives:

1. Timeline

2. Condition

3. Motivation

4. Reassure

The bot always starts with the highest priority objective, in this case, the timeline. It won't proceed to the next objective until it has completed the current one. After gathering the timeline information, the bot will move on to either condition or motivation. Here’s where the bot’s decision-making shines. If the conversation context suggests more information about the condition (e.g., the user mentions living in a beat-up house), the bot will prioritize the condition before motivation.

You'll notice priority levels within the Objective Section. Agent AI Chat Bot will move sequentially through these priority levels to accomplish objectives, but any objectives that have tied priority will be attacked in the conversational order that makes the most sense based on the conversation up to that point.

For example, in this image we can see the bot will always determine timeline first, then decide whether to move onto "motivation" or "condition" next.

Imagine the conversation about timeline went like this:

AI: Happy to see you're giving us a shot at being the buyer for your place. Mind if I ask when you're hoping to have the sale done?

Lead: As soon as possible, can't stand living in this mess anymore.

The lead already mentioned something relating to the property condition here, so the bot will likely move on to gathering details about the property condition before moving on to motivation.

Finally, the bot will complete the Give Info objective to reassure the contact.

Handling Tied Priority Levels: When objectives have the same priority level, the bot decides which one to tackle based on the conversational flow. This dynamic approach ensures that the interaction feels natural and addresses the user’s most pressing concerns first.

Max Attempt Limits: Each objective can have a maximum attempt limit. For instance, if the motivation objective has a max attempt limit of two, the bot will try twice to complete it. If it fails, it will move on to the next objective, ensuring the conversation continues smoothly without getting stuck.

Final Stage: Give Info: After attempting all higher-priority objectives, the bot moves to the final give info stage. This is where it reassures the user, providing relevant information to help them feel confident in their next steps.

Benefits of Objective Ordering:

• Improved User Experience: Ensures conversations are logical and flow naturally.

• Efficiency: Prioritizes tasks based on importance and conversational context.

• Flexibility: Adapts to the conversation, addressing the user's needs dynamically.

• Error Handling: Max attempt limits prevent the bot from getting stuck on a single task.

Conclusion: Objective ordering in AGENT AI Chat Bot is a powerful feature that enhances user interactions by prioritizing tasks intelligently. By understanding and utilizing this feature, you can ensure your chatbot provides a seamless and effective customer experience. Start leveraging objective ordering today and see the difference in your bot's performance!

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