There are many services wherein a single bot is not sufficient or can underperform; hence, the idea of multi-bot AI architecture evolved.
Bots have become like air now, omnipresent. According to a report by the FTC, 37% of the internet traffic today is due to digital bots. People are using bots to find their favorite tracks, call friends and colleagues, ask for routes, solve their queries related to products and what not. Many companies have started using bots to increase their business with a chatbot. There are many services wherein a single bot is not sufficient or can underperform; hence, the idea of multi-bot AI architecture evolved.
Benefits of a multi-bot approach
Customer can address everything from a Single Point or Conversation
From a user perspective, it can prove to be quite a bit of hassle in a case they need to have a conversation with bots as well as humans for solving a particular problem. For instance, for a billing related query, a chatbot would answer the most basic questions of a user and then schedule a call with the customer care for more detailed information. This leaves the customers waiting till some executive is available to speak to.
With a multi-bot architecture, a business can schedule multiple bots to do the heavy lifting in the backend. This will leave a single bot as the single point of communication for the customer.
Multi-bot model eliminates constraints from the business
Many times a single AI bot may prove to be insufficient in addressing the complexities of the modern business.
There is an upper bound on the capabilities of an AI bot in terms of the maximum numbers of topics that it can handle simultaneously.
If the models are designed in such a manner that they are targeted to solve a specific task then the AI bot’s upper bound limit is sufficient to handle all the possible business use cases. But if, there is a single Ai bot required handling multiple tasks together, then it will affect its performance.
If too many tasks are assigned to a single AI bot, then there are tradeoffs to be made between quality and functionality. This is why a multi-bot architecture is desirable so that too much pressure does not come upon a single bot.
Different bots for different tasks
With a multi-bot architecture, it is possible to assign a different bot for a different task. Different bots for different purposes help in establishing a good connection with customers by allowing the designers to design a separate personality for each bot. For instance, a bot that handles transactions should have a different approach than a chatbot for customer services. Having different personas for each bot can play a vital role in designing a smooth user journey.
While designing bots it is important to consider parameters like state and history. A stateless bot is a kind of bot that does not need to know what has gone before to respond. A good example of a stateless bot would be a simple FAQ bot.
A non- stateless bot, on the other hand, is a bot that needs to know the history in order to respond correctly. A journey bot is a good example of a state bot.
A multi-bot architecture allows various bots to work in harmony with each other so that the necessary tasks are executed with perfection. Using the multi-bot architecture, the artificial intelligence for business can be given a nitro boost.
While designing a multi-bot architecture, sufficient care needs to be taken to ensure disambiguation in the system. Every bot must know the action to be taken and each task should be assigned to an appropriately skilled bot.
With a multi-bot architecture, it is easy to keep adding more skills to the bots and expand the range of use cases.
An insurance company dealing in various kinds of insurance products can benefit from multi bot architecture. For example, when a customer asks, “I would like to buy insurance”, the system can respond “Would like insurance for your car, home or life?” Then, depending upon the user’s response, an appropriate bot can be assigned on the task.
Multi bot architecture aids in greater efficiency and consistency in customer experience. This is because with multiple bots, information sharing becomes easy as the bots can access shared resources like HR and IT.
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