Engineering guidance, not a report of client results. Code fragments illustrate architecture and require testing before use.
Engineering recommendations and attributed research context; no client performance result is claimed. Source review: .
The Standard RAG Ceiling
Retrieval-augmented generation is a useful starting point when a question can be answered from a bounded set of relevant passages. Agentic retrieval adds decisions about follow-up searches or tools. It introduces additional cost, latency and failure paths, so it should solve a demonstrated retrieval problem.
Start by examining unanswered tasks. Missing data, poor access filters or weak document parsing will not necessarily improve when an agent is added.
Why Arbitrary Chunks Destroy Global Context
Chunk boundaries can separate a statement from its definition, table header or exception. Preserve document structure where it matters and keep identifiers that let the application recover surrounding context.
Compare retrieval strategies against labeled questions. Test specific facts, comparisons and questions requiring wider context separately. Avoid treating a larger context window as evidence that all relevant information was found or correctly interpreted.
The Agentic Knowledge Architecture
A bounded retrieval agent can decide whether an initial result is sufficient, request a follow-up search and assemble evidence before answering. Define the tools it can use and how it determines when to stop.
Keep permissions in the retrieval layer, not only in the prompt. Cache and budget search work where appropriate. Log which sources were inspected and distinguish an empty result from a failed source system.
Graph-Augmented Entity Routing
The GraphRAG research explores graph-based organization and summarization for questions spanning a document collection. That is a different problem from finding a single matching passage; it does not establish that graphs are always better.
A graph also introduces entity resolution, update and provenance challenges. Before adding one, identify the relationships a user needs and how those relationships can be checked against source material.
Comparative Production Architecture
| Approach | Useful starting point | Additional burden |
|---|---|---|
| Passage retrieval | Specific source-grounded questions | Parsing, indexing and relevance checks |
| Multi-step retrieval | Questions needing follow-up evidence | Tool budgets and stopping rules |
| Graph-based retrieval | Relationships and collection-level summaries | Entity resolution and graph updates |
Evaluate all candidates on the same tasks. Discuss a document intelligence workflow when the choice depends on your corpus and operating constraints.
References
What the sources support
- GraphRAG investigates collection-level query-focused summarization. arXiv 2404.16130. Collection-level research, not universal superiority over passage retrieval.
- From Local to Global: A Graph RAG Approach to Query-Focused Summarization — Edge et al., research paper