# Hybrid Multi-Agent GraphRAG for E-Government: Towards a Trustworthy AI Assistant
## 3 Technical Architecture and Solution
### Agents Configuration  
The agentic configuration uses the GraphRAG pipeline, the embedding pipeline, and web search as tools, leveraging their distinct search methods and answers to provide a complete response. These pipelines maintain modularity across multiple applications in different contexts.  
Querying in this pipeline starts with distinguishing query processes in the individual pipelines, with the outputs of the three specialized retrieval pipelines combined: the first two focus on internal document searches based on indexed data, and the third on real-time web search. Each pipeline processes and indexes its respective data sources: internal documents based on embeddings, structured triples accompanied by semantic similarity, and live web content, outputting standardized formats suitable for multi-tool access. The pipelines are wrapped with input and output mappings to facilitate consistent interfacing within the agentic framework. This architecture ensures that each specialized retrieval pipeline remains modular while being seamlessly accessible by the final agent, promoting scalability and extensibility across different retrieval modalities.  
Afterward, we employed an agent-based RAG approach to dynamically utilize the three specialized tools, embedding search, graph search, and web search, for synthesizing a structured reasoning process and a comprehensive answer based on the user query. When a query is received, the agent triggers each tool separately with custom instructions presented in Table 1, collects insights from internal documents and the knowledge graph, and gathers additional information through real-time web searches. A customizable system prompt guides the agent's reasoning, using GPT-4.1 as the final generator for chat completion, enforcing a step-by-step thought process that separates internal search insights from web-based findings. Furthermore, the agent is instructed to highlight any conflicts detected between internal and web-derived information, ensuring transparency and robustness of the final answer.  
\begin{table}
\begin{tabular}{c p{284.5 pt}} \hline \hline
**Tool Name** & **Instructions** \\ \hline Embedding Search & Answer questions using information retrieved from an internal document store containing content about the European Union’s news, policies, laws, and actions. Answers are based strictly on retrieved documents using semantic similarity, with no assumptions. References are included as HTML links. \\ \hline Graph Search & Answer questions using structured information from a knowledge graph containing factual relationships about the European Union’s news, policies, laws, and actions. The graph includes relationships (triples) and the original source documents. Answers are grounded in these facts, with references provided as HTML links. \\ \hline Web Search & Retrieve potentially relevant information from the web. Results are based on live internet content and may include a variety of sources. The retrieved information is not guaranteed to be factual. References are provided as HTML links using either inferred titles or domain names. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Agents’ tool roles of configuration.  
By deploying this agentic query pipeline, combining multi-source retrieval, structured reasoning, conflict analysis, and modular tool integration, users benefit from a transparent, dynamic, and comprehensive information access system dedicated to complex domains such as e-government and policy research.