# Hybrid Multi-Agent GraphRAG for E-Government: Towards a Trustworthy AI Assistant
## 5 Conclusions  
This research proposes a multi-agent system integrating GraphRAG, a traditional RAG implementation, and Web Search, utilizing official European Commission news data, press releases, and statements published in the Press Corner. The primary objective was to build and apply modular GraphRAG architecture, operationalized within an e-government application, assisting a comprehensive AI assistant. By incorporating e-government-related textual data into a structured graph (head, relationship, and tail) and applying graph and subgraph fuzzy retrieval alongside raw content, the system aims to enable traceable generation, enhancing explainability, transparency, and alignment with public policy. This study introduces a hybrid, multi-agent GraphRAG framework tailored to the complexities of e-government applications. By combining symbolic graph traversal, dense semantic embeddings, and real-time web search within a modular, agent-based architecture, the framework addresses key limitations of traditional RAG systems, namely their flat document retrieval structure, lack of multi-hop reasoning, and limited explainability. Designed for transparency, traceability, and adaptability, the system enables context-aware and audit-ready responses grounded in both structured and unstructured public data. In addressing the first research question RQ1, how GraphRAG's modular architecture can be operationalized in e-government settings, we demonstrated that the framework's components (e.g., graph-based indexing, entity retrieval, and CoT-enhanced generation) can be effectively orchestrated through the Haystack platform. The resulting system is not only explainable through source-linked outputs and graph reasoning paths but also reproducible and policy-aligned. Using real data from the EC Press Corner, we validated the operational viability of GraphRAG in real-world institutional contexts. The multi-agent virtual assistant model is based on Haystack's pipeline orchestrator, with modular components developed from scratch for efficient data indexing in triple form using GPT-4.1-mini. The indexed triples are stored in a Neo4J (Aura) database. Embeddings are computed and used in a dedicated traditional embedding-based RAG pipeline, with associated metadata (title, URL, and date). In parallel, live web search capabilities are integrated using SerpAPI for real-time Google search and URL retrieval, with processing handled by dedicated Haystack components. In response to RQ2, exploring the impact of integrating GraphRAG with traditional RAG pipelines and web search, our results confirm that hybrid architecture improves factual grounding, coverage, and adaptability. While embedding-based retrieval provides fluent and semantically cohesive answers, GraphRAG supports structured reasoning and factual disambiguation and web search enriches responses with current and external context. This synergy enables the assistant to dynamically adapt to various query types (e.g., fact-based, exploratory, or comparative), expanding its utility across a range of public-sector use cases. The final multi-agent system, using GPT-4.1, combines all three pipelines to generate comprehensive, source-grounded answers using diverse input and output formats. Web search is leveraged to validate internal data sources and detect potential inconsistencies, reinforcing factual consistency and accountability. In addressing RQ3 and the agent-based orchestration, we show that multi-agent coordination significantly enhances answer quality and factual consistency. A rigorous LLM-based evaluation with GPT-4o and a custom faithfulness rubric, conducted over 60 representative questions (30 related to \"Clean Industrial Deal\", and the other 30 related to \"US tariffs\"), further clarified the contributions of each retrieval strategy. Specifically, although both Embeddings RAG and GraphRAG agents operated on the same underlying official EC dataset, GraphRAG achieved 95.1% for supported statements and 76.7% for fully supported answers versus 93.6% and 68.3% for Embeddings RAG.