Agentic Graph Retrieval-Augmented Generation for Auditable Commercial Registry Analysis

Arthur Capozzi; Dirk Helbing

cs.IRApr 15, 2026

Agentic Graph Retrieval-Augmented Generation for Auditable Commercial Registry Analysis

Authors: Arthur Capozzi, Dirk Helbing

Organizations: Computational Social Science, ETH Zurich, Zurich, Switzerland · Complexity Science Hub, Vienna, Austria

Abstract

Public commercial registries are formally open, yet their practical analysis remains difficult because relevant facts are scattered across millions of records that combine structured metadata, multilingual legal notices, temporal events, and entity aliases. This paper presents a controlled, tool-mediated agentic GraphRAG architecture for auditable natural-language analysis of such registries. The proposed pipeline transforms publications from the Swiss Official Gazette of Commerce into a Neo4j knowledge graph comprising over five million nodes and 4.7 million relationships. It combines deterministic ingestion of structured registry fields, LLM-assisted extraction of latent actors from unstructured notices, and a deterministic identity-resolution layer. An analytical agent operates on this graph through intent routing, restricted graph tools, bounded reflection, and state-machine-guided response synthesis. We evaluate the system using a multi-tier protocol covering answer quality, retrieval behavior, entity resolution, and multi-turn conversational performance. The complete architecture is compared with dense, lexical, and hybrid flat-retrieval baselines and with controlled architectural ablations. On a manually curated benchmark, graph-mediated retrieval increases factual correctness from 0.26 for the strongest flat-retrieval baseline to 0.83 for the complete system, with comparable improvements in relevance and completeness. Ablation results show that bounded reflection improves answer quality while intent routing and LLM-based graph enrichment improve reliability in difficult entity resolution tasks. An exploratory dashboard displays the graph evidence and execution traces underlying each response, allowing users to inspect how answers were produced.

Figures & tables

Figure 1: Data ingestion pipeline used to construct the knowledge graph. Phase 1 performs schema-driven ingestion to create strong Company, Person, and Event nodes from verified fields. Phase 2 applies an LLM to commercial texts to extract hidden actors as weak nodes under constrained JSON output. Phase 3 resolves identity through alphabetical tokenization, NameHub merging, and weak-node absorption. The final output is a deduplicated Neo4j knowledge graph.

Architecture Phase	Calls	Purpose	Configuration
1. Intent Router	1	Classifies user intent to dynamically restrict the available sandbox tools.	GPT-4o mini (2024-07-18) $T=0.0$
2. Reflection Loop	0–4	Autonomously structures JSON tool requests and interprets raw graph responses.	GPT-4o mini (2024-07-18) $T=0.0$ , tool calling
3. Response Synthesis	1	Formats the accumulated evidence according to UI constraints (e.g., Markdown tables or dossiers).	GPT-4o mini (2024-07-18) $T=0.0$ , tool calling
Note: Total calls per user query range from 2 to 6, bounded by the reflection-loop limit of four.

Table 1: Summary of LLM calls within the agent architecture.

Figure 2: Architecture of the analytical agent. In Phase 1, a zero-shot intent router restricts the available tools. In Phase 2, the bounded reflection loop selects secure Python endpoints, generates JSON arguments, queries Neo4j, and incorporates execution feedback. The state machine tracks the analytical trajectory and constrains the final response synthesis in Phase 3.

Figure 3: Cropped dashboard view in global-analysis mode. The center indicates that no entity is selected, while the global agent answers a registry-wide query and exposes its execution trace and returned records. The complete interface is shown in Supplementary Fig. S1.

Figure 4: Cropped dashboard view in dossier mode. The left panel displays entity-search results, while the selected company dossier presents registry attributes, legal purpose, associated personnel, and event history. The complete interface is shown in Supplementary Fig. S2.

Figure 5: Cropped dashboard view of network analysis. The central panel renders multi-hop corporate relationships, while the context-aware agent presents the retrieved connections and exposes the execution trace and raw records. The complete interface is shown in Supplementary Fig. S3.

Dataset Name	Size	Generation Method	Evaluation Focus
Graph-Seeded Automated Benchmark	300 questions	Graph-seeded generation	Agent trajectory (Tier 2), semantic quality (Tier 3)
Manually Curated Benchmark	60 questions	Author-curated	Mean Correctness score (Tier 3)
Conversational Benchmark	10 evaluated conversations / 36 turns	Author-curated	Context carryover and multi-turn coherence (Tier 4)

Table 2: Summary of the evaluation datasets used in Tiers 2, 3, and 4.

Metric	Result
NameHub Nodes Sampled	1,000
Grouped-Name Comparisons Evaluated	1,191
Orthographic Consistency Rate	96.22%

Table 3: Tier 1 NameHub orthographic-consistency diagnostic.

Extracted item	Precision	Recall	F1	Matching criterion
Persons	0.989	0.956	0.972	Fuzzy name match
Companies	0.933	0.651	0.767	Fuzzy name match, excluding structured subject entities
Roles	0.723	0.611	0.662	Coarse-compatible role match
Entity-role relations	0.701	0.482	0.571	Coarse-compatible entity-role match

Table 4: Manual validation of the LLM weak-node extraction stage on 120 manually reviewed SHAB notices.

Metric	Value
Questions Evaluated	300
Search-First Accuracy	55.7%
Fallback Activation Rate	2.0%
Average Reasoning Steps	1.7
Query Success Rate	100%
Average Latency	12.53 s

Table 5: Summary of Tier 2 evaluation metrics.

Architecture variant	Faith. $\uparrow$	Ans. rel. $\uparrow$	Info. recall $\uparrow$	Mean s $\downarrow$	Unsucc. $\downarrow$
Dense Vector-RAG	0.905	0.081	0.066	32.45	0.000
Lexical Full-Text RAG	0.975	0.047	0.030	0.88	0.940
Hybrid Dense+Lexical RAG	0.879	0.127	0.091	1.35	0.813
GraphRAG w/o reflection	0.894	0.425	0.388	5.54	0.053
GraphRAG w/o router	0.887	0.742	0.581	9.25	0.280
Structured-only GraphRAG	0.865	0.651	0.534	9.85	0.330

Table 6: All-architecture comparison on the graph-seeded automated benchmark (N=300). The unsuccessful-outcome rate is a benchmark-level diagnostic derived from recorded architecture-family-specific failure indicators; it is not a uniform API or runtime-error rate. A zero rate indicates that no configured failure marker was detected, not that retrieval or the final answer was correct. Failed rows remain included in the answer-quality means when valid judge scores exist.

Architecture variant	Correctness $\uparrow$	Ans. rel. $\uparrow$	Info. recall $\uparrow$	Mean s $\downarrow$
Dense Vector-RAG	0.143	0.247	0.118	15.62
Lexical Full-Text RAG	0.237	0.322	0.261	14.29
Hybrid Dense+Lexical RAG	0.262	0.470	0.237	17.50
GraphRAG w/o reflection	0.552	0.563	0.560	9.57
GraphRAG w/o router	0.708	0.777	0.703	10.72
Structured-only GraphRAG	0.545	0.733	0.578	16.63

Table 7: All-architecture comparison on the manually curated benchmark (N=60). Correctness replaces Faithfulness because verified reference answers are available for each question.

Architecture variant	Evidence-bearing	Supported outcome	Empty result	Tool/error	Retrieved documents / tool calls
Dense Vector-RAG	0.283	0.067	0.000	0.000	16.32
Lexical Full-Text RAG	0.300	0.133	0.667	0.000	4.62
Hybrid Dense+Lexical RAG	0.533	0.167	0.000	0.000	20.00
GraphRAG w/o reflection	0.583	0.550	0.133	0.150	1.00
GraphRAG w/o router	0.850	0.683	0.000	0.117	1.57
Structured-only GraphRAG	0.783	0.483	0.000	0.450	2.07

Table 8: All-architecture trajectory and evidence diagnostics on the manually curated benchmark (N=60). Evidence-bearing and supported-outcome rates are deterministic diagnostics computed from the recorded retrieved context and reference answers. Empty-result and tool/error rates use recorded architecture-family-specific fields. The final column reports retrieved documents for flat retrieval systems and tool calls for graph-based systems.

Architecture variant	Corr.	Ans. rel.	Info. recall	Turn succ.	Carryover	Tool trans.
Dense Vector-RAG	0.344	0.644	0.317	0.367	0.433	N/A
Lexical Full-Text RAG	0.342	0.493	0.345	0.308	0.400	N/A
Hybrid Dense+Lexical RAG	0.310	0.677	0.283	0.308	0.350	N/A
GraphRAG w/o reflection	0.458	0.598	0.450	0.450	0.517	0.800
GraphRAG w/o router	0.554	0.714	0.525	0.542	0.517	0.900
Structured-only GraphRAG	0.604	0.827	0.580	0.608	0.650	0.900

Table 9: All-architecture comparison on the conversational benchmark (10 conversations, 36 turns). N/A indicates graph-tool behavior not present in the corresponding flat retrieval architecture.

Extracted Role	Graph Edge Type
SUBJECT	HAS_EVENT
PARENT	HEAD_OFFICE_OF
SELLER	TRANSFERRED_TO
BUYER	ACQUIRED_FROM
DEBTOR_COMPANY	HAS_EVENT
DEBTOR_PERSON	INVOLVED_IN

Table S1: Mapping of extracted semantic roles to deterministic graph edge types during the ingestion pipeline.

Node Type	Strong	Weak	Total
Company	935,014	112,344	1,047,358
Person	57,508	606,392	663,900
Event	N/A	N/A	2,180,645
NameHub	N/A	N/A	1,354,406
Total Nodes			5,246,309
Total Edges			4,729,070

Table S2: Total number of edges and distribution of nodes by entity type and identification strength.

Semantic Intent (Router Output)	Activated Tools in Sandbox	Mechanism & Description	Routing Rationale (Problem Solved)
Entity Disambiguation	Entity-disambiguation, network-exploration, event-history, and lexical full-text endpoints	Searches for Company and Person entities through fuzzy matching over names, locations, and purposes. It excludes orphan weak nodes through the NameHub hierarchy.	Supports initial entity identification while retaining the network and history endpoints required for the next analytical step.
Network Exploration	Entity-disambiguation, network-exploration, event-history, and lexical full-text endpoints	Executes a three-branch UNION ALL Cypher query to identify corporate structures, event subjects, and lateral connections across NameHub aliases.	Keeps entity search available because network traversal requires a resolved target entity.
Event-History Retrieval	Entity-disambiguation, network-exploration, event-history, and lexical full-text endpoints	Traverses an entity through its NameHub and collects chronologically ordered Event nodes associated with its legal aliases.	Retains entity search so that the agent can resolve the target UID before retrieving its timeline.
Macro-Level Analytics	Top-entity aggregation, event-conditioned counting, and read-only custom-query endpoints	Provides statistical summaries by ranking entities by selected metrics (e.g., risk rank or nominal capital) or counting nodes associated with specific sub-rubrics.	Removes entity-search and traversal endpoints so that aggregation is performed by predefined server-side queries rather than manual pagination.
Deep Text Fallback (Nested in non-analytic intents)	Lexical full-text search endpoint	Runs a broad lexical search utilizing a Neo4j Lucene full-text inverted index directly against raw legal text snippets.	Provides a fallback when structured entity matching fails for an uncommon string or an entity that is not represented as a resolved graph node.
Unrestricted Multi-Hop Exploration ( all )	All 7 tools loaded simultaneously	Grants the agent access to the standard retrieval suite, analytics endpoints, and the read-only Cypher sandbox.	Supports ambiguous or multi-part questions that cannot be assigned reliably to a narrower intent category.

Table S3: Mapping of semantic intents to activated database tools, underlying Cypher mechanisms, and architectural rationales.

Figure S1: Complete dashboard in global-analysis mode, corresponding to Fig. 3 of the main paper. The left panel supports entity search, the center remains in global view because no entity is selected, and the right panel shows a registry-wide analytical result together with its execution trace and returned records.

Figure S2: Complete dashboard in dossier mode, corresponding to Fig. 4 of the main paper. Entity-search results remain visible on the left, the selected company dossier occupies the central workspace, and the context-aware agent appears on the right.

Figure S3: Complete dashboard during network exploration, corresponding to Fig. 5 of the main paper. The interface combines entity search, the interactive graph and dossier, and the context-aware agent with expandable execution evidence.

Metric	What it measures	Why it matters	Scoring prompt
Faithfulness	Whether the agent’s answer is fully grounded in the retrieved context, meaning that every factual claim in the answer can be traced back to the data the system actually retrieved from the database.	A fluent response may still contain claims that are absent from the retrieved evidence. A score of 1.0 indicates that all factual claims are supported by the context, whereas 0.0 indicates that the response is unsupported. This metric is used for the graph-seeded automated benchmark, where grounding to retrieved evidence is central to the evaluation.	“Given the context and the answer, compute a faithfulness score between 0.0 and 1.0. 1.0 means the answer is 100% derived from the context. 0.0 means the answer states completely fabricated facts. Output ONLY a float number.”
Correctness	Whether the agent’s answer is factually consistent with the verified reference answer, regardless of how the supporting context was retrieved.	Correctness is used for the manually curated benchmark, where each question has a verified reference answer. It measures factual agreement with that answer rather than only support from the retrieved context.	“Given the question, the expected answer, and the actual answer, compute a correctness score between 0.0 and 1.0. 1.0 means the actual answer is factually correct and captures the expected answer well. 0.0 means the actual answer is factually wrong or fails to answer the question correctly. Be tolerant to differences in wording, formatting, ordering, or level of detail, as long as the substance is correct. Output ONLY a float number.”
Answer Relevance	Whether the answer directly and completely addresses the user’s question, regardless of the retrieved context.	A factually supported response can still address the wrong question or obscure the requested information with irrelevant detail. Answer Relevance isolates whether the response directly addresses the question.	“Given the question and the answer, compute an answer relevance score between 0.0 and 1.0. 1.0 means it perfectly answers the question directly. 0.0 means it dodges the question entirely. Output ONLY a float number.”
Information Recall	The fraction of the expected answer’s core information that the agent’s response successfully captures.	Grounding, correctness, and relevance do not establish that an answer is complete. Information Recall compares the response with the reference answer and measures how much of its core information is present. It is therefore sensitive to incomplete retrieval and incomplete synthesis.	“Compare the Expected Answer to the Actual Answer. Score from 0.0 to 1.0 how much of the Expected Answer’s core information is present in the Actual Answer. Output ONLY a float number.”

Table S4: Answer-quality evaluation metrics, their mechanisms, operational importance, and corresponding LLM-as-a-judge prompts.

Metric	What it measures	Why it matters	Scoring prompt / logic
Turn Success Rate	The fraction of turns in a conversation that are successfully answered according to the conversational scoring rule.	A system may answer the final question correctly after making errors in earlier turns. Turn Success Rate captures consistency across the full interaction and reflects the propagation of local errors through later turns.	For each turn, success is assigned deterministically after the judge scores the answer. A turn is marked successful if the normalized exact match equals 1.0, or if Correctness $\geq 0.80$ , or if Correctness $\geq 0.70$ and Answer Relevance $\geq 0.70$ . The final Turn Success Rate is the average of these binary turn outcomes over all turns in the conversation.
Context Carryover Accuracy	Whether the system correctly preserves the active entity, prior references, and relevant facts across follow-up turns that require memory of earlier dialogue state.	Conversational systems may answer isolated questions correctly but lose track of the active person, company, event, or previously retrieved fact. This metric evaluates continuity across follow-up questions involving counting, summarization, or co-reference resolution.	This metric is computed deterministically over the subset of turns tagged for context carryover, entity memory, or temporal memory. For these turns, the same binary success rule used in Turn Success Rate is applied. Context Carryover Accuracy is then defined as the average success over this memory-sensitive subset only.
Tool Transition Accuracy	Whether the GraphRAG Agent follows the expected structured tool sequence for a given conversational turn.	The graph-based architecture assigns different tools to different semantic intents. Tool Transition Accuracy evaluates whether the agent follows the expected structured retrieval path, thereby testing the state machine and sequential tool logic.	This metric is only computed for the agentic GraphRAG. For each turn that specifies an expected tool behavior, the execution trace is inspected to determine whether at least one of the expected tools was actually called. A hit receives a score of 1.0 and a miss receives 0.0. Tool Transition Accuracy is the average of these binary hits across all turns for which an expected tool behavior is defined.

Table S5: Conversational evaluation metrics, their mechanisms, operational importance, and corresponding scoring logic.

Metric & Score	Question	Reference Answer	Agent Answer
Answer Relevance Score: 0.10	What is the registered address of the company with anonymized UID CHE-000.000.000?	Street Name 19, 5300 City A	The registered address of the company Company A GmbH (UID: CHE-000.000.000) has changed to City A as of January 12, 2024. Previously, it was located in City B . Would you like to investigate anything else related to this company?
Information Recall Score: 0.55	Within the NameHub ’Person X’, do the company links resolve to one or multiple distinct person profiles, and for each profile (by person_uid), which company is it associated with?	Multiple distinct profiles: two in total. weak_1 is associated with Company B Sàrl; weak_2 is associated with Foundation C.	The search for ”Person X” returned a weak node, which has been explored to reveal connections to other individuals. Here are the distinct person profiles associated with Person X, along with the companies they are linked to: • Person X: Company B Sàrl (Role: Associate) • Person X: Foundation C (Role: Board Member) Note: This entity is connected to a NameHub. You can open the NameHub to explore other records that might belong to the same underlying name cluster. Would you like to investigate further into any specific company or person?

Table S6: Examples of LLM-judge sensitivity on the graph-seeded automated benchmark.

Metric & Score	Question	Reference Answer	Agent Answer
Answer Relevance Score: 0.40	Show all the companies with which Person Y is connected.	Person Y is connected with the following companies: Person Y, Company D Sàrl.	The entities shown below are connected to various records sharing this exact name, but the database cannot determine with 100% certainty that they belong to the exact same physical person. • Company D Sàrl (Location: City C, Connection: HAS_EVENT, Date: 2023-12-20) • Person Y (Location: City C, Connection: HAS_EVENT, Date: 2023-12-12) Would you like to investigate one of these companies further?
Information Recall Score: 0.00	Have any notices of dissolution or bankruptcy been published for Company E?	Yes, a notice of dissolution for Company E was published on December 17, 2020.	The company ”Company E” located in City D has a history of bankruptcy. Here are the relevant details: 1. Bankruptcy Notice: The bankruptcy was officially opened by the Regional Court F, effective from December 10, 2020, at 14:02. 2. Previous Changes: On June 5, 2020, the company changed its name from ”Company G” to ”Company E” and relocated to Street Name 10, City D. 3. Initial Registration: The company was initially registered as ”Company G” on September 19, 2019, in City E. If you need more information or further investigation into related entities or events, please let me know!

Table S7: Examples of LLM-judge sensitivity on the manually curated benchmark. These cases illustrate how verbosity or technically accurate details, such as a legal effective date versus a publication date, can affect scores.

Metric	Value
Labeled examples	40
Pearson correlation	0.721
Spearman correlation	0.752
Mean absolute error	0.168
Within-half-point agreement	0.875
Quadratic weighted Cohen’s $\kappa$	0.702

Table S8: Human validation of GPT-5 judge scores on a stratified sample of answer-quality evaluations.

Support label	Count	Share
Supported	14	0.233
Partially supported	46	0.767
Not supported	0	0.000
Uncertain	0	0.000
Supported or partially supported	60	1.000

Table S9: Text-grounded validation sample for the graph-seeded automated benchmark. Labels reflect conservative provenance checks against original SHAB notice text and connected structured metadata; they are not independent human annotations.

Architecture variant	Family	Controlled configuration
Dense Vector-RAG	Flat retrieval	Dense retrieval over event documents using the Chroma index.
Lexical Full-Text RAG	Flat retrieval	Neo4j full-text index retrieval at the selected $k=20$ setting.
Hybrid Dense+Lexical RAG	Flat retrieval	Reciprocal rank fusion of Chroma dense and Neo4j lexical candidates at $k=20$ .
GraphRAG w/o reflection	Graph ablation	Full graph tools and router retained, but bounded recovery is limited to one retrieval/planning attempt.
GraphRAG w/o router	Graph ablation	Reflection and graph tools retained, but the agent receives the full tool set for every query.
Structured-only GraphRAG	Graph ablation	Router and reflection retained, but LLM-extracted weak nodes are ignored during retrieval.

Table S10: Complete architecture variant definitions used in the all-architecture benchmark comparison.

Architecture variant	N	Faith.	95% CI	Ans. rel.	95% CI	Info. recall	95% CI
Dense Vector-RAG	300	0.905	[0.877, 0.931]	0.081	[0.055, 0.109]	0.066	[0.043, 0.090]
Lexical Full-Text RAG	300	0.975	[0.959, 0.988]	0.047	[0.026, 0.072]	0.030	[0.016, 0.046]
Hybrid Dense+Lexical RAG	300	0.879	[0.848, 0.908]	0.127	[0.094, 0.164]	0.091	[0.065, 0.119]
GraphRAG w/o reflection	300	0.894	[0.866, 0.923]	0.425	[0.373, 0.477]	0.388	[0.339, 0.438]
GraphRAG w/o router	300	0.887	[0.865, 0.908]	0.742	[0.704, 0.782]	0.581	[0.540, 0.625]
Structured-only GraphRAG	300	0.865	[0.838, 0.892]	0.651	[0.607, 0.696]	0.534	[0.486, 0.582]

Table S11: Detailed graph-seeded automated benchmark results with bootstrap confidence intervals.

Architecture variant	N	Corr.	95% CI	Ans. rel.	95% CI	Info. recall	95% CI
Dense Vector-RAG	60	0.143	[0.075, 0.213]	0.247	[0.152, 0.346]	0.118	[0.064, 0.181]
Lexical Full-Text RAG	60	0.237	[0.147, 0.336]	0.322	[0.212, 0.437]	0.261	[0.158, 0.370]
Hybrid Dense+Lexical RAG	60	0.262	[0.176, 0.357]	0.470	[0.358, 0.581]	0.237	[0.157, 0.324]
GraphRAG w/o reflection	60	0.552	[0.428, 0.676]	0.563	[0.444, 0.680]	0.560	[0.435, 0.685]
GraphRAG w/o router	60	0.708	[0.598, 0.813]	0.777	[0.677, 0.868]	0.703	[0.592, 0.812]
Structured-only GraphRAG	60	0.545	[0.429, 0.659]	0.733	[0.629, 0.825]	0.578	[0.461, 0.695]

Table S12: Detailed manually curated benchmark results with bootstrap confidence intervals.

Architecture variant	N	Corr.	95% CI	Ans. rel.	95% CI	Turn succ.	95% CI	Carryover	95% CI
Dense Vector-RAG	10	0.344	N/A	0.644	N/A	0.367	[0.192, 0.558]	0.433	[0.250, 0.633]
Lexical Full-Text RAG	10	0.342	N/A	0.493	N/A	0.308	[0.175, 0.483]	0.400	[0.233, 0.567]
Hybrid Dense+Lexical RAG	10	0.310	N/A	0.677	N/A	0.308	[0.142, 0.508]	0.350	[0.183, 0.533]
GraphRAG w/o reflection	10	0.458	N/A	0.598	N/A	0.450	[0.292, 0.617]	0.517	[0.350, 0.683]
GraphRAG w/o router	10	0.554	N/A	0.714	N/A	0.542	[0.367, 0.708]	0.517	[0.350, 0.683]
Structured-only GraphRAG	10	0.604	N/A	0.827	N/A	0.608	[0.400, 0.808]	0.650	[0.467, 0.833]

Table S13: Detailed conversational benchmark results with bootstrap confidence intervals where available.

Architecture variant	N	Evidence	95% CI	Supported	95% CI	Empty	95% CI	Tool/error	95% CI
Dense Vector-RAG	60	0.283	[0.167, 0.400]	0.067	[0.017, 0.133]	0.000	[0.000, 0.000]	0.000	[0.000, 0.000]
Lexical Full-Text RAG	60	0.300	[0.183, 0.417]	0.133	[0.050, 0.217]	0.667	[0.550, 0.783]	0.000	[0.000, 0.000]
Hybrid Dense+Lexical RAG	60	0.533	[0.417, 0.650]	0.167	[0.083, 0.267]	0.000	[0.000, 0.000]	0.000	[0.000, 0.000]
GraphRAG w/o reflection	60	0.583	[0.450, 0.717]	0.550	[0.433, 0.683]	0.133	[0.050, 0.217]	0.150	[0.067, 0.250]
GraphRAG w/o router	60	0.850	[0.750, 0.933]	0.683	[0.567, 0.800]	0.000	[0.000, 0.000]	0.117	[0.050, 0.200]
Structured-only GraphRAG	60	0.783	[0.667, 0.883]	0.483	[0.350, 0.617]	0.000	[0.000, 0.000]	0.450	[0.333, 0.567]

Table S14: Detailed trajectory and evidence diagnostics with bootstrap confidence intervals where available. Reflection recovery is omitted here because it is a full-system-only diagnostic rather than a comparable all-architecture metric.

Result family	Primary recorded fields	Interpretation
Flat dense/lexical/hybrid runs	Retrieved-document counts, generated-answer text, explicit no-answer strings, API/judge errors	Captures empty retrieval, empty or explicit no-answer outputs, and execution errors; it does not represent graph-tool failures.
Graph-seeded graph-agent runs	Recorded trace logs, failed or empty tool-call indicators, recovery flags, generated answers, judge scores	Captures graph-tool retrieval failures or empty intermediate results; these may occur even when the final answer still receives a valid judge score.
Manually curated benchmark graph-agent runs	Recorded tool traces, answer text, judge scores, latency, and ablation-specific execution fields	Captures the same graph-tool reliability signals on the manually curated benchmark and keeps failed rows in aggregate quality means when scored.
Conversational runs	Turn-level answer text, trace fields, explicit no-answer strings, and graph-tool transition records	Captures turn-level unsuccessful outcomes; aggregate conversational metrics are computed separately at the conversation/turn level.

Table S15: Source-field definitions used for unsuccessful-outcome diagnostics. These diagnostics are comparable as benchmark-level reliability signals, but their raw source fields differ across architecture families because flat retrievers, graph agents, and conversational traces expose different execution metadata.

Architecture variant	Failed	Rate	Wilson 95% CI	Non-fail rel.	Non-fail recall
Dense Vector-RAG	0/300	0.000	[0.000, 0.013]	0.081	0.066
Lexical Full-Text RAG	282/300	0.940	[0.907, 0.962]	0.791	0.494
Hybrid Dense+Lexical RAG	244/300	0.813	[0.765, 0.853]	0.673	0.484
GraphRAG w/o reflection	16/300	0.053	[0.033, 0.085]	0.448	0.410
GraphRAG w/o router	84/300	0.280	[0.232, 0.333]	0.821	0.723
Structured-only GraphRAG	99/300	0.330	[0.279, 0.385]	0.821	0.758

Table S16: Graph-seeded benchmark-level unsuccessful-outcome rates with Wilson confidence intervals and conditional non-failure quality.

Comparison	N	Diff.	95% CI	A fail/F pass	A pass/F fail	Holm $p$
Dense Vector-RAG minus Full	300	-0.073	[-0.103, -0.047]	0	22	$<0.001$
Lexical Full-Text RAG minus Full	300	0.867	[0.823, 0.903]	262	2	$<0.001$
Hybrid Dense+Lexical RAG minus Full	300	0.740	[0.687, 0.790]	223	1	$<0.001$
GraphRAG without reflection minus Full	300	-0.020	[-0.053, 0.013]	9	15	0.307
GraphRAG without intent routing minus Full	300	0.207	[0.160, 0.253]	64	2	$<0.001$
Structured-only GraphRAG minus Full	300	0.257	[0.207, 0.307]	78	1	$<0.001$

Table S17: Paired graph-seeded unsuccessful-outcome comparisons against Full Agentic GraphRAG.

Architecture variant	Empty retrieval	Empty answer	Retrieval exec.	Recovery flag	No-answer
Dense Vector-RAG	0	0	0	0	0
Lexical Full-Text RAG	279	282	0	0	282
Hybrid Dense+Lexical RAG	0	244	0	0	244
GraphRAG w/o reflection	16	0	0	16	0
GraphRAG w/o router	84	0	0	84	0
Structured-only GraphRAG	98	0	1	99	3

Table S18: Failure-category decomposition for the graph-seeded benchmark. Categories are non-exclusive except for the primary category recorded in the machine-readable CSV.

Architecture variant	Failed rows	Faith. failed	Faith. non-failed	Failed faith. $\geq$ 0.9	Failed rel./recall $\leq$ 0.1
Dense Vector-RAG	0	N/A	0.905	0	0/0
Lexical Full-Text RAG	282	0.995	0.648	280	282/282
Hybrid Dense+Lexical RAG	244	0.882	0.863	200	243/243
GraphRAG w/o reflection	16	0.131	0.937	2	16/16
GraphRAG w/o router	84	0.841	0.904	55	13/35
Structured-only GraphRAG	99	0.726	0.934	53	46/81

Table S19: Faithfulness diagnostics on graph-seeded failed and non-failed rows.

Architecture variant	Grouping	Value	N	Non-fail recall
Dense Vector-RAG	difficulty	Level 1	100	0.078
Dense Vector-RAG	difficulty	Level 2	100	0.070
Dense Vector-RAG	difficulty	Level 3	100	0.049
Dense Vector-RAG	task type	Direct Entity Data	100	0.078
Dense Vector-RAG	task type	NameHub Entity Resolution	100	0.070
Dense Vector-RAG	task type	Temporal History	100	0.049

Table S20: Graph-seeded unsuccessful-outcome rates by difficulty and task type.

Bench.	Comp.	Metric	N	Delta [95% CI]	$p$
GS	FA–DV	Faithfulness	300	$-0.007\;[-0.043,\phantom{-}0.028]$	0.704
GS	FA–DV	Answer Relevance	300	$\phantom{-}0.609\;[\phantom{-}0.558,\phantom{-}0.658]$	$<0.001$
GS	FA–DV	Information Recall	300	$\phantom{-}0.508\;[\phantom{-}0.458,\phantom{-}0.561]$	$<0.001$
GS	FA–LF	Faithfulness	300	$-0.077\;[-0.104,-0.047]$	$<0.001$
GS	FA–LF	Answer Relevance	300	$\phantom{-}0.642\;[\phantom{-}0.592,\phantom{-}0.690]$	$<0.001$
GS	FA–LF	Information Recall	300	$\phantom{-}0.544\;[\phantom{-}0.497,\phantom{-}0.590]$	$<0.001$

Table S21: Paired answer-quality comparisons against Full Agentic GraphRAG across benchmark items. Benchmark abbreviations: GS, graph-seeded; MC, manually curated; CONV, conversational. Architecture abbreviations: FA, Full Agentic GraphRAG; DV, Dense Vector-RAG; LF, Lexical Full-Text RAG; HY, Hybrid Dense+Lexical RAG; NR, GraphRAG without reflection; NI, GraphRAG without intent routing; SO, Structured-only GraphRAG.

$k$	Faith.	Corr.	Rel.	Recall	Mean s	Median s	P95 s	Avg. Docs
5	0.858	0.082	0.142	0.064	16.97	16.23	23.26	4.65
10	0.875	0.103	0.197	0.082	16.61	16.09	24.43	9.25
20	0.887	0.112	0.201	0.086	16.86	16.44	24.17	18.50

Table S22: Vector-RAG answer-quality sensitivity by retrieval depth on the manually curated benchmark.

$k$	Faith.	Corr.	Rel.	Recall	Mean s	Median s	P95 s	Avg. Docs
5	0.974	0.134	0.195	0.117	13.57	13.24	19.04	1.67
10	0.992	0.215	0.312	0.221	16.53	15.67	28.37	2.98
20	0.977	0.237	0.322	0.261	14.29	13.92	21.05	4.62

Table S23: Lexical full-text answer-quality sensitivity by retrieval depth on the manually curated benchmark.

$k$	Faith.	Corr.	Rel.	Recall	Mean s	Median s	P95 s	Avg. Docs
5	0.909	0.195	0.310	0.171	20.13	17.73	34.26	5.00
10	0.963	0.203	0.404	0.169	16.78	15.67	24.13	10.00
20	0.958	0.262	0.470	0.237	17.50	16.62	24.78	20.00

Table S24: Hybrid dense+lexical answer-quality sensitivity by retrieval depth on the manually curated benchmark.

$k$	Token Recall	Token Hit@50%	Nonempty
5	0.259	0.217	1.000
10	0.279	0.233	1.000
20	0.300	0.233	1.000

Table S25: Retrieval-only top-

k

sensitivity for the vector baseline. No answer generation or LLM judging is used.

Metric	Operational definition	Value
Evidence-bearing retrieval success	Evidence in retrieved context	0.9500
Correct-answer supported trajectory	Correctness $\geq 0.80$ and evidence-bearing	0.8000
Non-evidence retrieval rate	Returned data, but no answer-bearing evidence	0.0500
Tool error / empty-result rate	Empty/error tool calls over all tool calls	0.0132
Reflection recovery rate	Later evidence after weak first retrieval	0.6000
Duplicate tool-call rate	Repeated same tool and arguments	0.0000

Table S26: Evidence-linked trajectory diagnostics for the full agentic GraphRAG on the manually curated benchmark. These diagnostics connect recorded tool traces to reference-answer evidence and existing correctness scores; they do not replace final answer-quality metrics.

Extracted item	Precision	Recall	F1	Matching criterion
Persons	0.989	0.956	0.972	Fuzzy name match
Companies	0.933	0.651	0.767	Fuzzy name match, excluding structured subject entities
Roles	0.723	0.611	0.662	Coarse-compatible role match
Entity-role relations	0.701	0.482	0.571	Coarse-compatible entity-role match

Table S27: Manual validation of LLM weak-node extraction on 120 manually reviewed SHAB notices. Main registered subject companies are excluded from the weak-company evaluation because they are captured by deterministic structured ingestion.

For 15 notices, the archived validation records did not provide a reliable language label, and the text-based heuristic could not assign one confidently. These notices are included in the aggregate 120-notice results but excluded from this language-specific comparison.
Language	$N$	Persons	Companies	Roles	Relations
French	36	1.000/1.000/1.000	1.000/0.846/0.917	0.800/0.696/0.744	0.765/0.542/0.634
German	36	1.000/0.929/0.963	0.714/0.556/0.625	0.829/0.723/0.773	0.818/0.540/0.651
Italian	33	0.966/0.966/0.966	1.000/0.538/0.700	0.658/0.532/0.588	0.571/0.400/0.471

Table S28: Language-specific manual validation of LLM weak-node extraction. Each metric cell reports precision/recall/F1. Person and company names use fuzzy matching; roles and entity-role relations use coarse-compatible matching.

Metric	Value
Company nodes evaluated	1,039,198
Unique official UIDs	806,520
NameHubs evaluated	908,203
True-positive pairs	114,556
False-positive pairs	41,837
False-negative pairs	157,006

Table S29: Company entity-resolution validation using official CHE UIDs as an external identity diagnostic. Pairwise recall is UID-continuity recall and should not be interpreted as true person/entity recall for the full NameHub layer.

Dataset	Metric	Graph Mean [95% CI]	$\Delta$	$\Delta$ 95% CI	$p$
GS	Faithfulness	0.898 [0.874, 0.920]	-0.007	[-0.044, 0.029]	0.693
GS	Answer Relevance	0.689 [0.646, 0.730]	0.609	[0.557, 0.658]	$<0.001$
GS	Information Recall	0.574 [0.528, 0.618]	0.508	[0.456, 0.561]	$<0.001$
MC	Correctness	0.828 [0.735, 0.913]	0.685	[0.565, 0.797]	$<0.001$
MC	Answer Relevance	0.887 [0.822, 0.941]	0.641	[0.517, 0.756]	$<0.001$
MC	Information Recall	0.838 [0.746, 0.919]	0.719	[0.611, 0.819]	$<0.001$

Table S30: Bootstrap confidence intervals and paired graph-minus-baseline deltas from the two-system comparison between the full agentic GraphRAG and the dense vector baseline. Dataset abbreviations: GS, graph-seeded automated benchmark; MC, manually curated benchmark; CONV, conversational benchmark.

Statistic	Value
Conversations	10
Total turns	36
Mean turns	3.60
Minimum turns	3
Maximum turns	4
Std. turns	0.49

Table S31: Descriptive statistics for the multi-turn conversational benchmark.

Dataset	System	$N$	Mean s	Median s	P95 s	Mean Tools	Max Tools
GS	Full Agentic GraphRAG	300	12.53	11.36	26.93	1.67	4
GS	Dense Vector-RAG	300	32.45	32.35	37.66	–	–
MC	Full Agentic GraphRAG	60	10.27	9.90	20.11	1.27	3
MC	Dense Vector-RAG	60	15.62	15.39	27.92	–	–
CONV	Full Agentic GraphRAG	10	30.60	28.37	61.63	3.70	7
CONV	Dense Vector-RAG	10	37.06	35.06	58.64	–	–

Table S32: Latency and tool-call statistics computed from the recorded evaluation traces. Dataset abbreviations: GS, graph-seeded automated benchmark; MC, manually curated benchmark; CONV, conversational benchmark. Token usage was not logged for these runs, so API cost is not estimated.

Category	Implementation identifier	Reader-facing terminology
Agent endpoint	search_companies	Entity-disambiguation endpoint
Agent endpoint	explore_network	Network-exploration endpoint
Agent endpoint	get_node_history	Event-history endpoint
Agent endpoint	global_text_search	Lexical full-text search endpoint
Agent endpoint	get_top_entities	Top-entity aggregation endpoint
Agent endpoint	count_entities_by_event	Event-conditioned counting endpoint

Table S33: Mapping between implementation identifiers and reader-facing terminology. Graph-schema labels and properties are intentionally excluded because they identify the database model.

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May 7, 2026, cs.AIJ/K move · Enter open · S save

AgenticRAG: Agentic Retrieval for Enterprise Knowledge Bases

Susheel Suresh, Hazel Mak, Shangpo Chou, Fred Kroon+1

Microsoft Corporation

Hievi-Rag Agentic

Open paper AskPDF arXiv

We present AgenticRAG, a practical agentic harness for retrieval and analysis over enterprise knowledge bases. Standard RAG pipelines place significant burden of grounding on the search stack, constraining the language model to a fixed candidate set chosen deep in the retrieval process. Our approach reduces this overdependence by layering a lightweight harness on top of existing enterprise search infrastructure, equipping a reasoning LLM with search, find, open, and summarize tools enabling the model to iteratively retrieve information, navigate within documents, and analyze evidence autonomously. On three open benchmarks we observe substantial gains:

49.6\%

recall@1 on BRIGHT (+21.8 pp over the best embedding baseline), 0.96 factuality on WixQA (

+13\%

relative improvement), and

92\%

answer correctness on FinanceBench--within 2 pp of oracle access to true evidence. Ablation studies show that the most significant factor is the shift from single-shot retrieval to agentic tool use (

5.9\times

improvement), while multi-query search and in-document navigation contribute to both quality and efficiency. We present various design choices in our agentic harness that were informed by pre-production deployments. Our results demonstrate its suitability for real-world enterprise production environments.

Abstract

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