OmniRAG-Agent: Multi-Agent RAG Systems

• OmniRAG-Agent is a multi-agent retrieval-augmented generation system combining hybrid vector-graph techniques with specialized LLM orchestrations for diverse QA challenges.
• It integrates dense, sparse, and graph-based retrieval with chain-of-thought reasoning to synthesize context-rich, efficient answers and improve key accuracy metrics.
• Optimized for domains like enterprise software testing and scientific literature review, it leverages RL and multi-turn planning to reduce redundant processing and enhance traceability.

OmniRAG-Agent refers to a family of multi-agent, agentic retrieval-augmented generation systems architected for robust, adaptable, and domain-general question answering and reasoning across diverse modalities, retrieval environments, and enterprise contexts. It embodies a convergence of multi-agent orchestration, hybrid retrieval (dense, sparse, and graph-based), explicit reasoning and planning, proactive context management, and rigorous optimization for performance, interpretability, efficiency, and compliance. This design is realized across a spectrum of use cases—ranging from low-resource, omnimodal audio-video reasoning (Zhu et al., 3 Feb 2026), agentic scientific literature review (Nagori et al., 30 Jul 2025), and SLA-driven enterprise QA (Iannelli et al., 2024), to enterprise-scale software testing automation and quality engineering (Hariharan et al., 12 Oct 2025).

1. System Architecture and Multi-Agent Orchestration

OmniRAG-Agent systems are architected around the modular, layered composition of specialized LLM-based agents, each responsible for distinct inference, retrieval, or reasoning sub-processes. The canonical architecture, exemplified in enterprise software QA (Hariharan et al., 12 Oct 2025), comprises four main strata:

• Hybrid Vector-Graph Knowledge System: Integrates a dense vector store (e.g., SingleStore, Pinecone) for semantic similarity search with a graph database (e.g., TigerGraph or Neo4j), capturing typed, weighted knowledge relationships such as “Requires,” “Depends on,” and “Covers.”
• Enhanced Contextualization Engine: Enacts multi-stage assembly: initial vector search, dynamic graph traversal to expand context, semantic and source-based synthesis, followed by conflict resolution—leveraging source credibility and temporal cues.
• Multi-Agent Orchestration Layer: Employs a suite of specialized agents—including Query Planner, Vector/Graph Retrieval, Context Assembler, Generation Orchestrator, and Traceability Agent—passing control and context through a message bus (e.g., Kafka, gRPC).
• Traceability and Audit Layer: Implements automatic trace matrices for bidirectional mapping among requirements, test cases, execution results, and change requests, supporting comprehensive traceability across the QA lifecycle.

This modular multi-agent paradigm generalizes to diverse domains, as seen in SIRAG’s Decision Maker and Knowledge Selector agents for process-supervised RL (Wang et al., 17 Sep 2025), and in the planner-step-definer-extractor-QA pipeline of MA-RAG for collaborative, chain-of-thought multi-hop reasoning (Nguyen et al., 26 May 2025). In omnimodal QA, the agent loop encompasses proactive planning, tool invocation (image/audio search), and evidence aggregation (Zhu et al., 3 Feb 2026).

2. Retrieval, Reasoning, and Evidence Integration

Retrieval in OmniRAG-Agent systems merges dense and symbolic signals to enable robust and context-rich augmentation for generation. The underlying retrieval score for each document $d$ with respect to query $q$ includes both semantic similarity and knowledge-graph relational salience. In enterprise settings:

$$s(q,d) = \lambda\,\mathrm{sim}_{\mathrm{vec}}(q,d) + (1-\lambda)\,\mathrm{rel}_{\mathrm{graph}}(q,d), \qquad 0 \leq \lambda \leq 1$$

where

$$\mathrm{sim}_{\mathrm{vec}}(q,d) = \frac{\langle \mathbf{v}_q, \mathbf{v}_d \rangle}{\|\mathbf{v}_q\| \|\mathbf{v}_d\|}$$

and

$$\mathrm{rel}_{\mathrm{graph}}(q,d) = \max_{p \in \mathcal{P}(q,d)} \sum_{e \in p} w_e - \gamma \cdot \mathrm{dist}(q,d)$$

This hybrid signal is integrated into context assembly, and further enhanced by multi-agent step-wise reasoning. MA-RAG-inspired pipelines decompose queries $Q$ via planning agents ($P \sim P_{\text{plan}}(P|Q)$), define subqueries for evidence collection, and iteratively refine retrieval using feedback signals from extracted evidence and chain-of-thought traces, yielding an end-to-end Bayesian-style answer integration: $$a^* = \arg\max_{a} \sum_{d,\tau,e} p_{\mathrm{qa}}(a|Q,d,\tau,e) \, p_{\mathrm{ext}}(e|d,\tau) \, p_{\mathrm{cot}}(\tau|Q) \, p_{\mathrm{retrieve}}(d|Q)$$ (Nguyen et al., 26 May 2025, Hariharan et al., 12 Oct 2025)

In omnimodal QA (Zhu et al., 3 Feb 2026), the RAG module indexes sampled image frames and ASR-segmented audio utterances, aligning retrieval and evidence fusion via CLIP or text encoders. Tool-calling and evidence merging follow an agentic, multi-turn protocol.

3. Dynamic Agent Loop, Planning, and Efficiency Optimizations

A defining feature is the dynamic, on-demand invocation of agents and tool-calls, guided by explicit uncertainty quantification, trajectory pruning, and process-level supervision. Both MA-RAG and SIRAG highlight runtime determination of agent calls: skipping planning or extraction when uncertainty is low, and orchestrating retrieval/generation only as warranted (Wang et al., 17 Sep 2025, Nguyen et al., 26 May 2025).

In RL-optimized agentic RAG (e.g., omnimodal QA (Zhu et al., 3 Feb 2026)), a group-relative policy optimization (GRPO) variant of PPO is adopted. The RL loss aligns policy improvement jointly over tool-use and answer accuracy, balancing exploration and efficiency through a customized advantage signal across question groups and executing early stop decisions contingent on evidence sufficiency. Notably, >30% reduction in unnecessary extractor or retrieval calls on single-hop queries is empirically observed in MA-RAG (Nguyen et al., 26 May 2025).

SIRAG’s process-level reward mechanism introduces an LLM-as-judge module that scores each intermediate step for consistency, utility, and non-redundancy, informing RL credit assignment. A tree-structured rollout strategy expands retrieval and selection actions as a decision tree, exploring alternative reasoning paths to collect granular feedback (Wang et al., 17 Sep 2025).

4. Domain-Specific Applications and Adaptations

OmniRAG-Agent demonstrates adaptability across domains:

• Enterprise Software Testing and Quality Engineering: Central use in automating test plan/case generation, regression suite design, and change-impact mapping, with achieved gains in accuracy (from 65.2% up to 94.8%), test suite efficiency (85% improvement), and operational cost (~35% savings), attributed to hybrid vector-graph retrieval, multi-agent contextualization, and rigorous traceability (Hariharan et al., 12 Oct 2025).
• Scientific Literature Review: Open-source frameworks implement agentic selection between GraphRAG (Cypher/Neo4j queries on structured bibliometrics) and VectorRAG (sparse+dense hybrid with re-ranking), with instruction tuning improving context recall and faithfulness metrics on synthetic benchmarks (Nagori et al., 30 Jul 2025).
• Omnimodal Audio-Video QA: Under tight compute/resource constraints, the agentic loop builds external image/audio banks, enables multi-modal retrieval, and coordinates evidence aggregation via multiturn, tool-calling LLM agents, achieving stepwise performance improvements (23.05 → 27.34% accuracy) compared to static baselines (Zhu et al., 3 Feb 2026).
• SLA-Driven and Reconfigurable Systems: SLA-aware planners manage dynamic reconfiguration for enterprise QA—modulating ensemble size, retrieval depth, and arbitration thresholds—to satisfy intent-specific constraints on answer quality, system cost, and latency (Iannelli et al., 2024).

5. Evaluation, Metrics, and Empirical Findings

Benchmarked evaluations across domains validate salient performance improvements:

Domain/Task	Baseline/Static	OmniRAG-Agent Variant	Delta	Key Metric Type
Enterprise QE (Hariharan et al., 12 Oct 2025)	65.2%	94.8%	+29.6%	Accuracy
Sci. Lit. Review (Nagori et al., 30 Jul 2025)	0.42 (Recall VS)	1.05	+0.63	Context Recall (VS)
Omnimodal AV QA (Zhu et al., 3 Feb 2026)	23.05%	27.34%	+4.29%	Test Accuracy
SIRAG QA (Wang et al., 17 Sep 2025)	37.55%	46.23%	+8.68%	Exact Match Accuracy
SLA-driven QA (Iannelli et al., 2024)	F₁ = 0.663 (N=3)	F₁ = 0.688 (N=5)	+0.025	F₁ Score (Ensemble Size)

Metric definitions include accuracy, exact match, F₁, context precision/recall, faithfulness (supported facts per answer), and cost/latency. Ablation studies repeatedly demonstrate significant drops in accuracy when hybrid retrieval, contextualization, agent orchestration, or traceability are removed (e.g., –18.2% for contextualization in QE (Hariharan et al., 12 Oct 2025)).

In both synthetic and production environments, bootstrapped standard errors and uncertainty intervals are reported (Nagori et al., 30 Jul 2025), and agentic designs offer improved answer verifiability, reduced hallucination/incongruence, and interpretable reasoning paths.

6. Challenges, Best Practices, and Engineering Lessons

Deployment and maintenance of OmniRAG-Agent systems introduce several practical considerations:

• Hybrid Knowledge Base Evolution: Requires ongoing synchronization between vector embeddings and evolving graph schemas. In contexts where business logic or schema rapidly change (e.g., SAP migration), continuous re-indexing and schema migration are necessary (Hariharan et al., 12 Oct 2025).
• Dynamic Model Routing: Efficient task allocation among lightweight (e.g., Mistral) and heavyweight (e.g., Gemini Pro) LLMs according to task complexity, using agentic orchestration for dynamic decision-making (Hariharan et al., 12 Oct 2025).
• Instruction Tuning: Direct Preference Optimization (DPO) on small, high-quality human-labeled sets yields tangible improvements in faithfulness and domain alignment, demonstrating the feasibility of lightweight fine-tuning in agentic pipelines (Nagori et al., 30 Jul 2025).
• Operational Constraints: Service-level-aware planners mediate the trade-off between quality and resource use. Increasing ensemble size benefits F₁ and reduces hallucination but incurs increased computational expense and latency (Iannelli et al., 2024).
• Validation Layers and Traceability: Multi-layer context validation (syntax, semantics, compliance, traceability) and trace matrices inform all major artifact flows, supporting regulatory, audit, and explainability requirements.

7. Future Directions and Open Extensions

Emerging research outlines several promising vectors for OmniRAG-Agent evolution:

• Arbitration Beyond Voting: Moving past simple majority or cross-encoder arbitration toward weighted or ML-based arbitration, potentially integrating learning-based resource planners (Iannelli et al., 2024).
• Hierarchical and Modular RL: Hierarchical PPO for synchronizing across agent timescales, dynamic tree-budgeting for adaptive depth, and modular plug-and-play agent extension (e.g., query rewriter, consistency verifier) (Wang et al., 17 Sep 2025).
• Uncertainty-Driven Human in the Loop: Integration of runtime retrieval/generation confidence as decision-hand-offs for human review, and uncertainty-calibrated prompt selection (Nagori et al., 30 Jul 2025).
• Omnimodality and Resource Adaptation: For low-resource, long-context QA, optimizing adaptive retrieval call budgets, integrating robust speech embeddings, and structured memory graphs for contradiction detection (Zhu et al., 3 Feb 2026).

OmniRAG-Agent thus represents a generalizable blueprint for high-performance, interpretable, and dynamically adaptive retrieval-augmented generation, leveraging a suite of coordinated agents to address the full spectrum of contemporary information-seeking and reasoning challenges.