# Retrieval-Augmented Generation to Generate Knowledge Assets and Creation of Action Drivers
## 3 Materials and Methods
### Agentic RAG  
Agentic RAG (see Figure 3) transcends the limitations of traditional RAG systems by incorporating AI agents that can perceive their environment, make decisions, and take actions autonomously [23]. While conventional RAG follows static workflows with limited adaptability, agentic RAG enables dynamic decision making and workflow optimisation through agent-based autonomy. This paradigm shift addresses challenges in multistep reasoning and complex task management that traditional systems struggle to handle. The fundamental distinction lies in how agentic RAG transforms the retrieval process from passive information retrieval to proactive problem solving. Traditional RAG systems are reactive data retrieval tools with no inherent ability to adapt to changing contexts, whereas agentic RAG transitions to adaptive, intelligent problem solving with self-assessment capabilities. The architecture of agentic RAG systems (Figure 3) typically involves autonomous agents that leverage key agentic design patterns, such as  
1. Reflection: Agents evaluate retrieved information and self-critique their outputs; \begin{table}
\begin{tabular}{l l} \hline \hline
**Step** & **Techniques** \\ \hline Preprocessing & Data cleaning, chunking, metadata annotation \\ \hline Indexing & Vectorisation, column-specific indexing, hybrid indexing \\ \hline Query Optimisation & Query parsing, column filtering, semantic matching \\ \hline Metadata Utilisation & Row-level tags, sheet-level metadata, hierarchical organisation \\ \hline Retrieval & Contextual retrieval, multihop retrieval, reranking \\ \hline Post-Retrieval & Answer extraction, aggregation, formatting \\ \hline Generator Optimisation & Prompt engineering, template-based responses, error handling \\ \hline Automation & Automated updates, API integration, version control \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of techniques for improving RAG models. Planning: Systems break down complex queries into manageable subtasks;  
3. Tool use: Agents can access external resources beyond the initial knowledge base;  
4. Multiagent collaboration: Networks of specialised agents work together, checking each other's work. These systems operate on iterative refinement cycles that progressively improve both the relevance and diversity of retrieved documents. The autonomous agents dynamically manage retrieval strategies and adapt workflows to meet complex requirements, enabling unparalleled flexibility and context awareness. In general, AI agents follow the cycle of (see Figure 4) Thought, Action, and Observation [24]. 1. Autonomous Agent Integration: Agentive Retrieval-Augmented Generation (RAG) [23] integrates autonomous AI agents capable of reflection, planning, tool use, and multiagent collaboration. These agents operate beyond static retrieval pipelines by dynamically adapting retrieval strategies based on intermediate outputs and evolving task demands. This ag  
Figure 4: ReAct: reasoning action cycle for AI Agents. Figure 3: Retrieval agent: personalised tools are provided to the retrieval AI agent to automate the process to retrieve and generate the best response. and task-specific optimisation, making the system more responsive and intelligent in handling diverse and evolving information needs. 2. Iterative Refinement: A key differentiator of Agentic RAG is its iterative approach to information retrieval. Rather than relying on a single retrieval step, the system performs multiple refinement cycles, progressively improving both the relevance and diversity of retrieved documents. For example, Vendi-RAG [25] introduced a new diversity metric--the Vendi Score--alongside large language model (LLM) judges to evaluate and enhance the quality of responses across iterations. This iterative refinement mechanism leads to more robust and contextually nuanced outputs. 3. Context-Aware Retrieval: Agentic RAG systems place significant emphasis on semantic and structural context.