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A TigerGraph-backed fraud investigation system can give AI agents a shared view of how accounts, customers, transactions, devices, identities, and counterparties connect. The available TigerGraph materials describe several ways to provide graph context to agents, but they do not document a built or evaluated system with 11 agents. The 11-agent roster below is therefore a proposed design—not a report of an existing deployment or a claim of measured results.
What graph context adds to a fraud investigation
Fraud is often a relationship problem. An account viewed alone may look ordinary; its connections to devices, phone numbers, addresses, merchants, transactions, and other accounts can reveal a wider pattern. A graph represents those entities and links explicitly, so an investigation can follow paths across them instead of relying only on separate scores or records.
TigerGraph’s fraud materials illustrate this with a healthcare scenario connecting a provider, treatment center, administrators, addresses, and patient claims. The example uses an eight-hop query to explore relationships; that is an illustration, not a recommended hop count for every investigation. The useful distance depends on the case, graph design, and query limits.
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How graph retrieval and vector search fit together
Graph retrieval and vector retrieval answer different questions. Graph traversal finds connected entities and paths: for example, which accounts share a device with an account under review, or which transactions link two counterparties. Vector search finds semantically similar text, which can help locate relevant case notes, policy passages, or investigation documents.
TigerGraph’s Victor Lee wrote in “Agentic AI Architecture: How Graph Databases Power Intelligent Agent Systems” (August 18, 2026), “Graph retrieval should complement, not replace, vector search.” In practice, an agent workflow can retrieve graph facts and relevant documents, then present both—with provenance and limits—in a common investigation context. Similar text is not proof of a relationship, and a graph path is not proof of intent.
A proposed roster for 11 agents
The following roster is a design recommendation for a supervised workflow. It assumes a coordinator that delegates bounded tasks to specialist agents and assembles their results. It is not a TigerGraph-provided roster, nor evidence that this particular arrangement has been implemented or tested. In a production design, several roles could instead be tools or deterministic services rather than separate language-model agents.
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| Agent | Proposed responsibility | Guardrail |
|---|---|---|
| 1. Case coordinator | Accept the investigation request, break it into bounded tasks, track task status, and assemble outputs. | Cannot decide that a subject is fraudulent or take enforcement action. |
| 2. Intake and scope agent | Normalize the case request, identify the known subject and time window, and flag missing inputs. | Must distinguish supplied facts from assumptions; ask for clarification when identity or scope is ambiguous. |
| 3. Entity-resolution agent | Review candidate links among customer, account, device, identity, phone, and address records. | Report match evidence and confidence; do not silently merge entities or treat a weak match as confirmed. |
| 4. Graph-retrieval agent | Run approved graph queries for direct links and bounded paths around the subject. | Use allowlisted query patterns, documented limits, and source identifiers for returned facts. |
| 5. Transaction-pattern agent | Inspect relevant transaction sequences, timing, counterparties, and repeated behaviors. | Describe observed patterns; do not infer intent from a pattern alone. |
| 6. Network-expansion agent | Explore whether connected accounts, devices, or counterparties form a broader cluster relevant to the case. | Respect depth and size limits; label indirect connections and avoid treating association as guilt. |
| 7. Document-retrieval agent | Find semantically relevant policies, case notes, and other authorized unstructured records through vector retrieval. | Return document identity and passages; distinguish retrieved text from verified facts. |
| 8. Policy agent | Compare the available evidence with applicable internal policies or investigation criteria. | Cite the applicable policy version and identify any rule it cannot resolve. |
| 9. Evidence-synthesis agent | Combine graph results, transaction observations, and document passages into a structured evidence summary. | Keep provenance attached to each claim and surface conflicting or missing evidence. |
| 10. Challenge and quality agent | Look for alternative explanations, unsupported leaps, stale data, and contradictions in the proposed findings. | Challenge both positive and exculpatory evidence; do not replace investigator judgment. |
| 11. Case-draft agent | Prepare a reviewable case narrative with sources, open questions, and suggested next investigative checks. | Draft only; an authorized investigator reviews and approves any consequential decision. |
This separation is useful when tasks need different permissions, retrieval methods, or review rules. It is not inherently better than a single agent calling well-designed tools. Each additional agent adds orchestration, testing, security, and failure-handling work.
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Which integration pattern should you choose?
TigerGraph’s architecture guidance describes four patterns: one agent calling graph retrieval, multiple agents using shared graph memory, iterative GraphRAG retrieval, and MCP-connected access to a graph. These are implementation options, not comparative test results. The table describes practical trade-offs to assess for a particular workflow.
| Pattern | Best fit | Shared context and freshness | Trade-offs to assess |
|---|---|---|---|
| Single agent with a graph retrieval tool | A bounded investigation task with one reasoning flow and a limited number of graph lookups. | One agent holds the working context; the tool can retrieve current graph data when called. | Simpler orchestration, but a long task can become difficult to audit or maintain if retrieval and reasoning are tangled together. |
| Multiple agents with shared graph memory | Workflows that divide investigation tasks among specialists needing a common view of entities and evidence. | Agents can refer to shared state, subject to how the system governs updates and access. | Define which outputs become shared facts, how conflicts are handled, and how changes are logged. Shared memory must not turn an unverified claim into accepted evidence. |
| Iterative GraphRAG | Questions where initial retrieval may expose entities or paths that lead to follow-up graph queries. | Context is refined across retrieval iterations; freshness depends on each retrieval and the data source. | Set bounds on iterations, query scope, and result size. More retrieval can add latency and irrelevant paths as well as useful context. |
| MCP-connected graph access | An agent environment that benefits from a standardized way to expose graph tools or resources to a model. | Can provide access to live graph functionality, depending on the server and deployment. | Standardized access does not by itself provide authorization, safe query design, auditability, or correct results; those controls still need to be implemented. |
Choose based on task complexity, the number of agents, data freshness needs, and whether agents require shared context. Also assess document retrieval, evidence auditability, permission boundaries, latency, and the effort to build and maintain orchestration. Start with the simplest pattern that can meet the investigation’s requirements.
How to build the system without overstating what it knows
- Define the investigation boundary. Specify which case types the workflow supports, who may initiate it, the records it may access, and which decisions remain with an investigator. Set retention and access rules before connecting models to sensitive data.
- Model the entities and relationships. Identify the records needed for the use case—potentially customers, accounts, transactions, devices, identities, phone numbers, addresses, merchants, and counterparties. Define relationship meaning, source systems, timestamps, and identifiers. Do not encode a suspected association as a confirmed relationship.
- Set entity-resolution rules. Document what makes records a candidate match, which matches require human confirmation, and how uncertain or conflicting identifiers are represented. Measure false matches and missed matches before relying on resolved links in case summaries.
- Build narrow, auditable retrieval tools. Create approved graph queries for the investigation questions, with limits on time range, path depth, result volume, and permitted data. Return source identifiers and relevant timestamps with each result. Give agents only the access needed for their tasks.
- Add document retrieval separately. Index only authorized documents for vector retrieval. Preserve document identity and passage context so investigators can inspect the underlying material. Keep graph relationships and text similarity clearly labeled as different kinds of evidence.
- Implement orchestration and shared state. Decide which role calls each tool, what information moves between agents, how conflicts are surfaced, and what happens when a tool fails or returns no results. Treat agent-generated summaries as claims requiring provenance, not as new source records.
- Test against reviewed cases. Use cases whose evidence and investigator conclusions have been independently reviewed. Check retrieval relevance, entity resolution, omitted counterevidence, unsupported claims, consistency, latency, and cost. Compare results with a non-agent baseline where feasible; do not treat vendor outcome figures as expected performance for this implementation.
- Deploy with investigator approval and monitoring. Show evidence paths and source records in the case interface, log queries and agent outputs, provide a way to correct errors, and require an authorized person to make consequential decisions. Monitor drift in data, matching behavior, and retrieval quality.
What an investigator should see
A useful case view should separate underlying records from the system’s interpretation. For each material claim, show the linked entities and path, the source record or document, relevant timestamps, and whether the link is confirmed or uncertain. A summary should also identify missing data, alternative explanations, and evidence that does not support the leading theory.
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That presentation makes the graph useful as an investigation aid rather than an opaque verdict generator. TigerGraph’s June 29, 2026 article “Graph Database for Risk and Fraud Analytics: Why Unified Fraud Detection Beats Siloed Systems” supports the broader case for connected risk and fraud data while cautioning that a graph does not replace specialized platforms. Keep existing controls and decision systems in view when defining the graph’s role.
What is established—and what is not
TigerGraph’s materials support general patterns for connecting graph retrieval to agent workflows and illustrate relationship-based fraud analysis. They do not identify the duties or prompts of an actual 11-agent system, its orchestration framework, model choices, graph schema, deployment geography, governance controls, evaluation, or production results. Accordingly, the roster and build sequence here are a proposed blueprint, not a claim that a specific system exists or has demonstrated an outcome.
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