## 4 Proposed Chatbot Development Methodology
### Conversational Workflow  
After modeling and developing a virtual assistant in Dialogflow CX, a client can converse with the agent, which operates using a knowledge base from a bank agency. The knowledge base could, for example, be built upon expertly curated content such as the technical support script example utilized by an ATM operator to solve problems related to the cash dispenser as illustrated in Table 3. As the user interacts with the virtual assistant, a complex sequence of processes occurs within Dialogflow CX, involving many different components.  
These processes enable the agent to comprehend the user's input, determine the following appropriate action, and respond accurately and contextually. In addition, by harnessing machine learning and natural language processing, the agent can emulate a human-like conversation and provide an engaging and helpful user experience. Figure 2 offers a graphical representation of the various components and processes within Dialogflow CX, illustrating the operational process that leads to an accurate response for the user.  
Figure 1: The proposed methodology.  
Figure 2: Conversational workflow.  
The method involves the following steps:  
1. _User initiates the conversation_: The user starts a conversation with the agent by either typing a message or speaking to a voice assistant device. For instance, a customer may voice concerns related to an ATM dispenser, such as \"withdrawal unavailable\" or \"payer module stopped\".
2. _Agent comprehends user input_: The agent employs natural language understanding (NLU) to identify the user's intent and extract relevant entities from the input provided. For example, recognizing issues related to an ATM dispenser from our knowledge base. If the user's entry does not match any intent in the system, an event handler called \"no-match\" is triggered, prompting the user to rephrase the request.
3. _Agent determines the next action_: Using the identified intent and entities, and drawing from its knowledge base (Table 3), the agent leverages its trained machine learning models and conversational transition rules to determine the appropriate next course of action in response to the user's input. The agent also stores relevant conversational data based on the current state of the dialog.
4. _Agent responds to the user_: The agent formulates an appropriate response to the user's input, derived from its knowledge base. The response could be structured or unstructured data. In the structured case, the response is conveyed through textual communication, such as steps to resolve the ATM dispenser issue. In contrast, in the unstructured case, it can be conveyed via customized payloads, such as buttons, images, or videos.
5. _User initiates a subsequent interaction_: The user provides another input based on the agent's response, to which the agent must respond appropriately, considering all previous interactions and information from the knowledge base.
6. _Agent retrieves the pertinent conversation variables_: The agent retrieves the relevant conversation variables stored in step 3, using these alongside the knowledge base to inform subsequent interactions and repeats steps 2 to 5 until the conversation is successfully resolved or ended.  
In conclusion, a knowledge base similar to the one illustrated in Table 3 forms an essential part of the conversational workflow, aiding in more accurate and useful interactions between the user and the agent.