# Hybrid Multi-Agent GraphRAG for E-Government: Towards a Trustworthy AI Assistant
###### 1 Introduction  
As public institutions increasingly adopt AI-driven virtual assistants to promote transparency, accountability, and citizen engagement, the demand for language systems that are explainable, factually accurate, and context-aware has become critical. These systems must operate reliably in domains where outputs may influence regulatory compliance, budget decisions, or public opinion, such as in policy administration, social services, and legislative communication [1, 2]. To support these needs, retrieval-augmented generation (RAG) frameworks have gained traction as a means to improve the factual grounding of Large Language Models (LLMs) by incorporating external knowledge at inference time. While RAG enhances accuracy in open-domain question answering, its reliance on flat, unstructured document retrieval significantly limits its applicability in structured environments, particularly thosefound in e-government systems, where data is multilingual, multimodal, and relationally complex [3; 4]. Emerging from this limitation is GraphRAG, an approach that leverages structured knowledge representations, such as knowledge graphs to support multi-hop reasoning, entity disambiguation, and explainable answer generation. By organizing data as interconnected nodes and relationships, GraphRAG enables LLMs to extract subgraphs relevant to a query and to traverse semantic paths that reflect contextual and temporal relationships--an essential capability in domains such as finance, healthcare, and, crucially, public administration [5; 6; 7; 8]. Nonetheless, even GraphRAG frameworks struggle to handle live updates, open-domain ambiguity, and adaptive reasoning over heterogeneous data. In response to these limitations, this study introduces a hybrid, multi-agent GraphRAG architecture designed to meet the specific challenges of e-government applications. Our framework combines symbolic graph traversal, dense embedding-based retrieval, and real-time web search, orchestrated via a modular, agent-based pipeline. Each retrieval method complements the others: embedding search is efficient for direct factual queries, graph-based retrieval supports structured and multi-step reasoning, and web search enables real-time access to dynamic, external content. Public-sector datasets, including legislative texts, policy reports, multilingual press releases, and statistical records, are often interconnected thematically and temporally. Conventional RAG systems, which treat such data as flat text, risk omitting crucial context hierarchies and entity relationships. In contrast, our framework enables graph-aware indexing, contextual subgraph retrieval, and Chain-of-Thought (CoT)-based generation strategies, enhancing answer traceability and interpretability [9; 10]. Another concern is hallucination, where LLMs generate plausible yet unsupported claims. In government settings, such errors may lead to misinformation or public distrust. To address this, we introduce multi-agent architecture in which specialized agents for graph traversal, dense search, verification, and web retrieval collaboratively cross-validate results and trace their reasoning steps. This architecture increases system robustness and aligns outputs with regulatory expectations, such as those outlined in the European AI Act [11; 12]. To validate our framework, we evaluate it using a representative e-government dataset--the European Commission's (EC) Press Corner--by constructing a graph-based knowledge index and combining it with semantic and real-time retrieval modules. This study builds upon our previous work on multimodal RAG in Eurobarometer survey analysis, extending it to a fully operational hybrid pipeline capable of delivering explainable, transparent, and policy-compliant AI assistance [5]. The rest of our study is organized as follows. Section 2 discusses the evolution of RAG and GraphRAG, particularly in the context of public-sector data. Section 3 describes the proposed multi-agent architecture and technical implementation.