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What the knowledge graph does—and what it does not
A knowledge graph represents entities, their properties, and the relationships between them. For example, a support workflow might need to connect a customer to a contract, the contract to a product, and the product to a policy. A graph can make those explicit links queryable; vector retrieval can find passages whose meaning resembles a question. Using both lets an application draw on relationships as well as semantic similarity.
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The graph is only one part of the system. It does not replace the agent runtime that chooses what to do, the original documents needed to verify facts, the authorization checks that limit access, or tests that establish whether retrieval and actions are reliable. A graph also does not guarantee that extracted relationships are correct.
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Choose a workflow before choosing a graph
Start with one recurring task and specify who uses it, which records it may access, what actions it may take, and what counts as a successful result. A useful candidate involves links that are difficult to recover from isolated passages—for instance, finding which active contracts are affected by a product change and which policy governs each contract.
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Compare the simplest workable approaches before adding components:
| Design choice | Best fit | What it adds | Main trade-off |
|---|---|---|---|
| Standard RAG over one index | Questions that can be answered by retrieving relevant passages from one well-organized collection | Semantic search and grounded generation over source text | Less modeling and orchestration; may not represent multi-step entity relationships explicitly |
| Graph-enhanced retrieval | Questions that require connections among people, products, contracts, policies, events, or other entities | Graph records and relationship-aware retrieval, often alongside vector search | Requires a suitable schema, relationship validation, and ongoing graph operations |
| Fixed retrieval pipeline | A known workflow with a predictable retrieval sequence | Consistent, predefined steps for gathering context | Less flexible when the next useful query depends on what earlier results show |
| Agent-selected, iterative retrieval | Tasks where the system must decide which retrieval or business tool to use next | A reasoning loop that can call tools, inspect results, and seek more evidence | More orchestration, latency, token use, and failure modes to manage |
These are separate decisions: a graph can be queried through a fixed pipeline, and an agent can use tools without a graph. Microsoft Learn’s Azure Architecture Center says, “If your queries are straightforward enough that a single search against a single index can resolve them, standard RAG is the better fit. Each agent reasoning step adds latency, token consumption, and complexity.” That is a useful constraint, not a claim that one architecture wins for every task.
Define the knowledge model before scaling ingestion
Write down the entity types and relationships the chosen workflow actually needs. For a contract-impact workflow, an initial model might include Customer, Contract, Product, Policy, and ChangeEvent, with relationships such as “customer has contract,” “contract covers product,” and “policy governs product.” These are illustrative modeling choices, not a universal schema.
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For each important entity and relationship, decide which properties matter, who owns the information, and which source supports it. Preserve source identifiers and enough provenance to trace a graph fact or retrieved passage back to an origin record. A universal provenance format is not established by the reference designs; the right fields depend on the source systems and the audit requirements of the workflow.
Google’s reference architecture treats graph creation and text segmentation as distinct but linked preparation steps. It also cautions that generic entity extraction may not fit specialized domains such as healthcare or pharmaceuticals. Treat model-generated extraction as a draft: validate the vocabulary, relationship direction, ambiguity rules, and error consequences with domain owners before relying on it, especially for high-consequence data.
Build ingestion as a traceable pipeline
A practical preparation flow accepts source records or files, extracts or maps entities and relationships, writes graph records, segments the source text, creates embeddings, and connects the embedded passages to relevant graph context. Keeping graph structure and text linked makes it possible to traverse relationships while also retrieving the wording that supports an answer.
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- Receive and identify source data. Record stable source IDs and any permissions or ownership metadata that downstream retrieval must respect.
- Extract or map facts. Use existing structured fields where available; use extraction for unstructured material, with validation appropriate to the domain.
- Check and write graph records. Apply the agreed entity and relationship model, handle duplicates and updates, and retain links to the originating material.
- Segment and embed source text. Create searchable text chunks and embeddings, then associate them with their source and relevant graph entities.
- Observe processing outcomes. Make failures, rejected extractions, stale records, and update status visible so operators can diagnose ingestion rather than treating the graph as a self-correcting store.
This flow follows the linked graph-and-text preparation pattern described in Google’s reference architecture. The specific validation rules, provenance fields, and monitoring design are implementation decisions; the reference pattern does not define one universal pipeline.
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At query time, the application can embed the question, retrieve semantically related graph nodes or text, traverse relevant relationships, assemble supporting passages and graph context, rank the evidence, and generate a grounded response. A fixed workflow can define that sequence in advance. An agentic workflow can instead choose a retrieval tool, inspect its result, decide whether the evidence is sufficient, and call another tool when needed.
Keep the response tied to retrievable evidence. Where the answer depends on a relationship, show or retain the path used to establish it; where it depends on a policy or contract clause, retain the supporting passage and source. If evidence conflicts, is missing, or falls outside the user’s permissions, the application should say so or request an authorized next step rather than filling gaps with plausible-sounding text.
Give the agent narrowly scoped tools
Expose retrieval functions and business actions as separate tools with precise descriptions. A useful description names the system or collection the tool accesses, the kind of query it supports, and its intended scope. Microsoft recommends descriptions that identify the data source and scope so the agent can select tools appropriately.
Descriptions help the model choose; they are not security controls. Enforce authorization when a tool executes, using the user’s permissions and the applicable system rules. Do not rely on a prompt to prevent unauthorized reads or writes. Start with read-only retrieval, then add specific actions only after the permission boundary and evidence path are clear. Require human confirmation for consequential or difficult-to-reverse actions when the workflow calls for it.
AWS’s enterprise architecture treats security, observability, and discoverability as concerns that span system layers. In practice, record which tools were called, what access decision was made, and whether an action succeeded, while avoiding unnecessary exposure of sensitive data in logs.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Keep working context, durable knowledge, and audit history separate
“Memory” covers different jobs. Google describes long-term knowledge, working context, and audit history as distinct needs; AWS likewise presents agent memory and knowledge bases as system components. Design each for its purpose rather than keeping everything in one undifferentiated conversation transcript.
- Working context: Low-latency state needed to continue the active task, such as the current contract being examined or which step is complete.
- Durable knowledge: Approved organizational facts and deliberately distilled user or task preferences that should remain useful across sessions. Keep these distinguishable from unverified conversation claims.
- Transactional audit history: A durable record of consequential operations, including the relevant request, authorization decision, tool call, and result. This is an operational record, not a substitute for the knowledge graph or source documents.
Evaluate the graph and the agent as separate parts
Test the system on representative tasks before expanding its access or autonomy. Include questions that require relationship traversal and questions that do not; compare graph-enabled retrieval with a simpler baseline on the relationship-dependent cases. The architecture sources describe patterns, not independent benchmarks: they do not establish a universal accuracy gain, speedup, productivity increase, or cost saving from adding a graph.
- Retrieval relevance: Did the system find the right passages and entities for the question?
- Relationship correctness: Were the relationships and traversal path supported by source material?
- Grounding: Can a reviewer trace material claims to their origin records?
- Permission handling: Did retrieval and tools respect the requesting user’s access?
- Action authorization: Were writes limited to allowed actions and handled with the required confirmation?
- Operational behavior: Track latency, token consumption, failures, and the effort required to maintain schemas, indexes, and connected services.
Use failures to locate the layer that needs attention: an incorrect entity may point to extraction or schema problems; a missing passage may point to segmentation or indexing; a wrong relationship may point to graph data or traversal; an unauthorized result is a permissions defect, not a prompt-quality issue. Re-test after changing the affected layer.
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Decide whether the added complexity earns its place
Graph-enhanced retrieval is justified when the workflow repeatedly depends on explicit connections among records and those connections can be modeled and checked. It is a poor default for a task already served by one index, and it is not a shortcut around source quality, access control, or evaluation. The sound build is the smallest governed system that reliably completes the chosen workflow: add relationship structure where it answers a real retrieval need, and leave the rest simple.
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