Why graph design matters for enterprise AI agents
Enterprise RAG often runs into limits when simple has to answer questions that depend on relationships or time order. A problem that needs several connected steps cannot usually be fixed just by using a better or by putting more data into a flat Pinecone or Milvus index. This has pushed teams toward GraphRAG, but traditional can create heavy engineering work.
Running a such as Neo4j or AWS Neptune means teams often need to define a fixed ontology and build custom Python extraction pipelines for each document type. If a business team changes a folder layout or a custom CRM field, the pipelines can break, entities can be duplicated, and graph queries can fail. Keeping this working may require a dedicated graph engineering team.
A managed is presented as a more practical direction because it reduces the schema burden.
Key points
- Simple can struggle with questions that depend on relationships or time order.
- Multi-step data problems are not solved just by improving the .
- Traditional often need a fixed ontology and custom extraction pipelines.
- Changes in folders or CRM fields can break graph pipelines and queries.
- Managed s are presented as a way to reduce schema work.