## 3 Results, Case Studies, and Applications
### Conversational Pipeline and Agents' thought Process  
The thought process of the conversational pipeline, which was enabled by [PERSON]'s framework, applied a systematic and iterative approach to assembling comprehensive information about the Green Deal from multiple sources, adjusting its query to each subsequent source and seeking additional details and different perspectives to ensure a thorough and multi-faceted answer, all of which are initiated from the prompt configuration [36], as presented in Table 1. Initially, as the user suggested, it started with a broad query to Press Corner, obtaining a foundational understanding of the Green Deal and recognizing that the initial information provided a basic definition. After obtaining the initial information, it recognized the need for more comprehensive data and shifted to Web Search, refining the query to gather specific policy details and goals. With foundational and policy information obtained, it moved to Research to acquire detailed descriptions of the various focus areas and investment strategies, enriching the overall understanding. Lastly, it queried In Memory to capture strategic initiatives, objectives, and regulatory aspects, ensuring all essential elements were included.  
Regarding the \"What is the EU4 DigitalUA\" example, it adjusted its query to each subsequent source, seeking additional details and different perspectives to ensure, as requested, a thorough and multi-faceted answer initiated from the prompt configuration, as presented in Table 1. Initially, as the user suggested, it started with a query to Press Corner, obtaining foundational and detailed information about the EU4 DigitalUA project, specifically highlighting the \"DigiUni\" initiative to develop a digital platform for Ukrainian universities. Recognizing that this provided an educational perspective, it shifted to Web Search and querying to gather broader context and specific details about the project's role in Ukraine's digital transformation and its integration with the EU Digital Single Market. With these critical aspects covered, the pipeline moved to Research. However, this step did not yield additional information, indicating that the topic was not included in the related index that the Research Agent used. Lastly, it queried In-Memory documents, which did not provide further details, suggesting that the uploaded document was unrelated to the EU4 DigitalUA. Despite these gaps, the process ensured that all available information was included by integrating insights from the Press Corner and web search, resulting in a comprehensive understanding of the EU4 DigitalUA project.  
This methodical process ensured that each query was informed by the previous findings, progressively building a more detailed and comprehensive answer. The conversational pipeline and the agents' ability to adapt their queries based on the gathered information highlight its thoughtful and strategic approach to information retrieval, resulting in a final answer that integrated diverse perspectives and detailed insights from all agents and sources, providing a well-rounded overview of the Green Deal and the EU4 DigitalUA. Moreover, to prevent the system's hallucination, the GAI models were strictly instructed to respond solely based on the provided documents from retrieval (\"The AI Agent should ignore its knowledge when answering the questions\"). Additionally, if the retrieved documents did not contain the required information for Q&A, the assistant would answer with \"Final Answer: inconclusive\", as configured in the Prompt in Table 1; the example results for this behavior are the Research and In memory Agents' responses for the EU4 DigitalUA topic. However, the system's behavior is highly correlated with the overall performance of the GAI models. Therefore, system hallucinations should be regularly checked and monitored in accordance with the selected GAI model.