Agentic RAG
An architecture that combines Retrieval-Augmented Generation with agentic behavior, allowing an AI system to decide what to retrieve, from which sources, in what sequence, and to iterate its retrieval strategy when initial results are insufficient.
What It Is
Standard RAG retrieves documents based on a query and passes them to an LLM to generate a response. The retrieval is a single pass: query in, documents out, answer generated. It works well when the query is clear, and the relevant documents are likely to surface from one semantic search.
Agentic RAG adds reasoning to the retrieval process. The AI decides what to look for, evaluates whether what it found is sufficient, formulates a refined query if not, searches again, possibly in a different source, and continues until it has enough to answer confidently. The retrieval is a loop, not a single step.
This matters when the answer to a question requires synthesizing information across multiple documents, when the initial query might not capture the right terminology, or when different parts of the answer live in different systems, such as product specs in a PIM, pricing in an ERP, and contract terms in a document repository.
In B2B Commerce Context
B2B queries are often compound. A customer asking about delivery options for a specific product to their specific address under their specific contract is asking a question whose answer requires pulling from four different data sources. Standard single-pass RAG will surface generic information. Agentic RAG will identify that it needs product data, logistics data, address-zone mapping, and contract terms and retrieve each in turn.
For OroCommerce deployments, agentic RAG is the architecture that makes an AI assistant genuinely useful for complex catalog and account queries. It allows the assistant to reason: ‘I need to check product specs first, then check whether this customer’s contract includes that product category, then check current stock.’ Each step informs the next.
The practical improvement over standard RAG is accuracy on multi-condition queries. A distributor’s customer asking ‘What is the fastest way to get 200 units of product X to our Dallas facility under our Q4 contract pricing?’ needs agentic retrieval to answer correctly. A single semantic search against a vector database will not find all the pieces.
When You Need It
- Your AI assistant is accurate on simple, single-topic queries but consistently fails or gives incomplete answers on compound questions.
- Your data lives across multiple systems with different retrieval mechanisms, including a vector database for product descriptions, an API for live pricing, and a document store for contracts.
- You need the AI to reason about what it does not know and go find it, rather than answering confidently from an incomplete context.
- Query latency is acceptable in exchange for accuracy, since agentic RAG takes longer than single-pass RAG because it involves multiple retrieval steps.
Standard RAG is the right starting point. Upgrade to agentic RAG when the accuracy ceiling of single-pass retrieval is clearly visible in production.
What It Is Not
- Agentic RAG is not just a bigger vector database. Increasing the number of documents indexed or the context window size helps single-pass RAG but does not give it the ability to reason about what to retrieve next. The agentic element is the decision loop, not the data volume.
- It is not the same as having multiple RAG calls in a pipeline. A fixed sequence of retrievals, always retrieve product data, then always retrieve pricing, is not agentic. Agentic means the AI decides whether and what to retrieve next based on what it has found so far.
- It is not necessary for every RAG deployment. Single-pass RAG handles the majority of enterprise AI query types reliably and with lower latency. Agentic RAG is for the subset of queries that are genuinely multi-source and compound, usually the most valuable queries, but not the most common.
Comparison
| Dimension | Standard RAG | Agentic RAG |
|---|---|---|
| Retrieval passes | One | Multiple, adaptive |
| Query adaptation | Fixed | Reformulated based on results |
| Multi-source | Possible but not dynamic | Dynamic selection across sources |
| Latency | Lower | Higher (worth it for complex queries) |
| Best for | Single-topic queries | Compound, multi-condition queries |
See also
Ready to see it in action?
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