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Build an AI employee as a governed application, not as a language model with a longer prompt. Give it a defined workflow, approved tools, durable organizational knowledge and a way to retrieve both relevant documents and relationships among the people, products, contracts or events in those documents. A knowledge graph helps when the workflow depends on those relationships; it does not replace the agent runtime, source documents, access controls or evaluation.
Decide whether the workflow needs a graph
Start with one recurring job, a specific user and a clear boundary around what the system may read or do. For example, an employee might need to answer questions about how a customer’s contract, products, support events and internal policies relate. That is different from finding the most relevant paragraph in one clean document collection.
A graph represents explicit entities and relationships: a customer has a contract, a product is covered by a policy, or an event is associated with an account. Graph retrieval can follow those connections; vector retrieval finds text that is semantically similar to a question. GraphRAG combines the two so the system can use both relationship structure and relevant passages.
- Use a graph when: questions require connecting multiple kinds of entities, following relationships, or explaining how records are linked.
- Start with standard RAG when: one search against one index can answer the workflow’s questions. A graph adds modeling and operational work that may not improve this task.
- Test before expanding: compare graph-enabled retrieval with a simpler baseline on representative questions that actually require relationships.
Microsoft Learn’s Azure Architecture Center recommends standard RAG for straightforward questions answerable with one search against one index. It also notes that each agent reasoning step adds latency, token use and complexity. A graph layer and an agentic reasoning loop are separate choices: a workflow may need graph retrieval without allowing the model to plan repeated tool calls.
#1 Best Overall
Choose the architecture before choosing components
Keep ingestion and question answering as distinct paths. Ingestion turns approved source material into searchable text and structured knowledge. The serving path retrieves evidence for a request and, if authorized, invokes business tools. The following options describe different design decisions, not mutually exclusive products.
| Decision | Simpler starting point | When to add complexity |
|---|---|---|
| Knowledge retrieval | Vector or keyword search over one document index. | Add graph records and relationship traversal when answers depend on connections between entities. |
| Retrieval control | A fixed pipeline retrieves evidence in a predictable sequence. | Let the agent select and repeat retrieval tools when a task genuinely needs iterative investigation; account for additional latency, token use and complexity. |
| Memory | Keep only the active task context needed to complete the request. | Add separately managed durable knowledge, deliberately distilled user or task facts, and transactional audit history when those needs exist. |
| Deployment | Use the organization’s existing managed services where they meet data, access and operations requirements. | Separate graph and vector infrastructure, or build a custom runtime, only when the workflow’s requirements justify the added management effort. Google’s reference architecture notes that separate infrastructure can require more management and may cost more; it does not establish a universal cost comparison. |
These are architecture trade-offs, not evidence that one vendor or arrangement is universally best. The available vendor-published designs illustrate component boundaries and implementation flows; they are not independent benchmarks.
Model the knowledge the workflow needs
Define the graph schema before extracting records at scale. Keep it narrow enough to serve the chosen workflow, and specify what counts as an entity, a relationship and a trustworthy source for each fact.
Specify entities, relationships and properties
- Entities: the types of things the workflow needs to identify, such as customers, contracts, products, policies or events.
- Relationships: meaningful links between those entities, expressed with clear direction and names, such as “customer signed contract” or “event concerns product.”
- Properties: useful details attached to entities or relationships, such as an identifier, status or effective date.
- Ownership and provenance: which system or document is authoritative for a fact, and how a retrieved fact can be traced back to that origin.
Do not treat a model-generated graph as automatically correct. Google’s reference architecture separates graph creation from text segmentation and cautions that generic entity extraction may not fit specialized domains such as healthcare or pharmaceuticals. Treat extraction as a draft that needs validation against the domain vocabulary and the consequences of an incorrect relationship.
Build ingestion as a traceable pipeline
A practical ingestion path converts source data into linked graph and text representations. Google’s reference architecture describes these preparation steps as distinct but connected; the exact storage and processing choices depend on the deployment.
- Receive approved files or records. Identify the source system and retain stable source identifiers so that downstream results can be checked against origin material.
- Extract or map entities and relationships. Apply the defined schema. Validate extracted records, especially in specialized or high-consequence domains, rather than assuming a general-purpose extractor understands local terminology.
- Store graph records. Preserve the entity and relationship structure needed for the workflow, including whatever source references are required to trace facts back to documents or records.
- Segment source text. Split documents into useful passages while retaining the link to their source and relevant graph context.
- Create embeddings and connect them to graph data. Store vector representations for semantic retrieval and associate each passage or vector with the appropriate graph records. This lets the serving path combine matching text with relationships.
- Monitor processing and validate outputs. Track failed or incomplete processing, and sample extracted entities, links and source associations against the originals before relying on the index.
The linked graph-and-text flow supports provenance as an implementation choice, but the reference architecture does not prescribe a universal provenance schema. Define the identifiers and source references that your own users, auditors and support teams need.
Serve answers with evidence from both retrieval paths
At question time, the system can use semantic search to locate related passages and graph queries to identify connected entities. It then assembles and ranks the evidence before generating a response. A useful response should make clear what its source material supports rather than treating a plausible connection as a verified fact.
A fixed graph-and-vector retrieval flow
- Receive the user’s request and apply the appropriate identity and permission context.
- Embed the query or otherwise prepare it for semantic retrieval.
- Retrieve semantically related passages or graph nodes.
- Traverse relevant relationships to find connected records within the allowed scope.
- Assemble and rank passages and graph-derived facts, keeping their source references available.
- Generate an answer grounded in that assembled evidence; if evidence is missing or conflicting, surface the limitation rather than inventing a relationship.
This fixed sequence is often easier to operate and evaluate than giving the model open-ended discretion over every retrieval step. If the task requires investigation across several systems, an agent can instead choose among retrieval tools, inspect results and call another tool when evidence is insufficient. Microsoft describes this as an agentic retrieval reasoning loop. Use it when the added flexibility serves the workflow, not just because an agent can make multiple calls.
Give the employee scoped tools, not unrestricted access
Retrieval functions and business actions should be exposed as explicit tools with narrow purposes. A model’s ability to request an operation is not authorization to perform it: enforce permissions when the tool executes, using the user’s applicable access rights and the organization’s policy.
Rank #4
Describe each tool precisely
- Name the system, collection or business function the tool accesses.
- State what types of queries or operations it supports and what is out of scope.
- Separate read-only retrieval from actions that change business records.
- Apply authorization and input validation at execution time rather than relying on the model’s instructions.
- Record consequential operations in an audit trail that can be reviewed independently of the conversation.
Microsoft recommends tool descriptions that identify the data source and intended scope. AWS’s enterprise architecture treats security, observability and discoverability as concerns across system layers, rather than features to bolt on only at the interface. Begin with retrieval and a clear permission boundary; add actions only when that boundary is working and the action has an explicit authorization path.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Separate working context, durable knowledge and audit history
“Memory” covers several different jobs. Keeping them in one undifferentiated chat transcript makes it difficult to control freshness, access, retention and accountability.
The Tool Desk
Outbyte Driver Updater FREEFix the driver behind crashes, sound loss and screen glitchesFind Drivers →Outbyte PC Repair FREEClear out junk files and repair common Windows errorsFree Scan →- Working context: low-latency state needed during the active task, such as the current question, retrieved evidence and intermediate results.
- Long-term knowledge: durable organizational facts and source material maintained through the ingestion and governance process.
- Distilled personalization: selected user or task facts worth retaining, kept separate from raw conversation history and handled under an explicit policy.
- Transactional audit history: durable records of consequential operations, including what was requested and what was executed, retained according to the organization’s requirements.
Google describes long-term knowledge, working context and audit history as distinct data needs; AWS likewise treats agent memory and knowledge bases as system components. Their roles should remain distinct even if a particular deployment stores more than one in the same underlying service.
Best Value
Evaluate the employee before deployment
There is no established independent benchmark in the cited architecture material showing that a knowledge graph improves accuracy, productivity, speed or cost for every workload. Treat the following as an evaluation plan, not as a promised performance gain.
Test the questions and controls that matter
- Retrieval relevance: does the system retrieve useful passages and records for representative requests?
- Relationship correctness: are entity links accurate, and does traversal return the right connected records rather than merely plausible ones?
- Grounding: can a reviewer trace material claims back to source material, and does the answer acknowledge missing or conflicting evidence?
- Permission handling: can users retrieve only the information their access allows, including through graph traversal?
- Action authorization: are changes blocked when the user or request is not authorized, and is permitted activity auditable?
- Operational impact: what latency, token use and maintenance burden result from graph retrieval and, if enabled, repeated agent tool calls?
Compare the graph-enabled design with a simpler retrieval baseline on the same relationship-dependent questions. If the graph does not help with the workflow’s actual needs enough to justify schema maintenance and operational overhead, keep the simpler design.
What the architecture evidence can—and cannot—establish
The Google, AWS and Microsoft materials cited here are vendor-published architecture guidance. They describe patterns and product-oriented component boundaries; they do not provide an independent, cross-provider comparison or measured results for your workload. They therefore support a way to structure and evaluate a build, not a claim that graphs always outperform ordinary retrieval or that a specific platform is the right choice.
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