# Hybrid Multi-Agent GraphRAG for E-Government: Towards a Trustworthy AI Assistant
## 3 Technical Architecture and Solution
### Faithfulness Assessment Configuration  
To systematically evaluate the factual accuracy and reliability of answers generated by the various retrieval-augmented generation (RAG) pipelines, a rigorous and transparent LLM-based faithfulness assessment was applied across all systems, with the prompt configuration presented in Table 14 in the Appendix A. For each question in the evaluation set, the generated answer was first broken down into individual factual statements. These statements were then meticulously compared against the context provided by the system's information retrieval step to determine whether each claim was explicitly supported by the available evidence, unsupported or contradicted, or if the answer appropriately acknowledged uncertainty due to insufficient information. This comprehensive process not only measured factual correctness but also investigated the model's ability to recognize the limitations of its retrieved knowledge.  
For the basic pipelines, embedding search, graph search, and web search, this evaluation methodology was applied independently. Each pipeline represents a distinct retrieval strategy, namely semantic similarity retrieval from an internal document store (embedding search), structured querying over a knowledge graph (graph search), and live information gathering from the internet (web search). In each case, the retrieved context served as the reference point for evaluating the generated answers. Statement-level scores were assigned to indicate whether the information was supported, unsupported, or inconclusive (with a further distinction between justified and unjustified uncertainty).  
In addition to detailed statement-level analysis, question-level metrics were calculated, such as the proportion of answers that were fully supported by context and the frequency of unsupported or prematurely inconclusive claims. This enabled a robust and immediate comparison of the factual alignment across all simple pipelines. Moreover, for each generated answer, latency statistics were recorded, offering valuable insights into the system's efficiency and real-world responsiveness.  
For the agentic pipeline, the evaluation followed the same faithfulness base, but with additional attention to the agent's multi-step reasoning and its dynamic tool use, with the prompt configuration presented in Table 15 in Appendix A. Unlike the basic pipelines, the agentic approach can select among the embedding search, graph search, and web search tools at each step of its reasoning process, using them as building blocks to iteratively gather, cross-check, and synthesize information before arriving at a final answer, enabling more complex reasoning strategies.  
The faithfulness assessment thus examined both the grounding of each factual statement in the retrieved evidence and the effectiveness of the agent's use of the available tools. Furthermore, latency statistics were recorded for each answer, providing insight into the efficiency and real-world responsiveness of the agentic pipeline, in addition to its factual robustness. This comprehensive and unified evaluation framework ensures a transparent and meaningful comparison between both simple and agentic RAG approaches.