# Hybrid Multi-Agent GraphRAG for E-Government: Towards a Trustworthy AI Assistant
## 5 Conclusions
### Future Work  
Building on the foundation laid by this study, future work will focus on further optimizing domain-specific performance and expanding the framework's applicability. The next aim for future work would be to construct an RAG LLM-based evaluation system that would categorize and mark the answers' output from each dedicated pipeline and tool, based on the retrieved documents from which the answer was generated. In this way, we would be able to assess its performance on a very detailed level, detect hallucinations, oversights, and any conflicts that were found or missed between sources. Moreover, fine-tuning system components, such as entity extraction, retrieval scoring functions, and graph traversal strategies, will be critical for specialized domains like healthcare regulation, climate policy, or digital infrastructure governance. Additionally, there is significant potential to enhance multilingual and multimodal capabilities by incorporating vision-language models for interpreting infographics, charts, and tables, and by extending language coverage to include lower-resourced EU languages.  
Another important direction involves improving system transparency through explainability interfaces. The development of visual graph-based provenance tracking and dashboard-style interfaces will enable both technical users and public stakeholders to audit and trust system outputs, aligning the architecture with emerging regulatory frameworks, and keeping the system up to date based on the European AI Act is also a priority. This will involve integrating features such as automated risk classification, logging mechanisms for accountability, and differentiated access control for role-specific assistant usage.  
Looking as we scale the system across larger corpora and longer time horizons, we also aim to evaluate its capacity to manage knowledge drift, perform cross-period analysis, and support longitudinal question answering in institutional contexts.  
Overall, this research marks a step toward reliable and transparent AI assistants that are not only capable of handling complex and structured information but also responsive to the ethical, linguistic, and regulatory demands of modern e-government services.  
**Author Contributions:** Conceptualization: G.P., M.M. and V.S.; methodology: G.P. and M.M.; software: G.P.; validation: M.M., C.T., G.P. and V.S; formal analysis: V.S. and G.P.; investigation: G.P. and V.S.; resources: M.M., C.T., G.P. and V.S.; data curation: M.M., G.P. and V.S.; writing, original draft preparation: [PERSON] and [PERSON]; writing, review and editing: [PERSON], [PERSON], [PERSON] and V.S.; visualization: [PERSON] and V.S.; supervision: [PERSON] and [PERSON]; project administration: [PERSON], [PERSON], V.S. and [PERSON]; funding acquisition, (not applicable). All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Data Availability Statement:** The original data presented in the study are openly available on GitHub at [[https://github.com/gpapageorgiouedu/Hybrid-Multi-Agent-GraphRAG-for-E-Government](https://github.com/gpapageorgiouedu/Hybrid-Multi-Agent-GraphRAG-for-E-Government)]([https://github.com/gpapageorgiouedu/Hybrid-Multi-Agent-GraphRAG-for-E-Government](https://github.com/gpapageorgiouedu/Hybrid-Multi-Agent-GraphRAG-for-E-Government)) (accessed on 31 May 2025).  
**Conflicts of Interest:** The authors declare no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. This manuscript complies with ethical standards.