Traditional RAG systems are designed around a simple idea: find the most relevant information, add it to the prompt, and generate an answer. This works well for questions where the answer can be found in a small set of relevant documents.
The challenge appears when questions require more than retrieval. A user might ask, “How did Q3 revenue growth compare to last quarter’s guidance?” Answering that requires finding revenue numbers, locating previous guidance, comparing the two and deciding whether the evidence is sufficient. A fixed retrieval pipeline has no built-in mechanism for planning these steps or recovering when the first search does not return enough information.
Agentic RAG introduces an agent that manages the retrieval process. Instead of following a fixed path, the agent can reason about the task, break complex questions into smaller steps, choose different retrieval strategies, retry failed searches and validate the final response against available sources.
In this guide, we’ll first review how traditional RAG works, explore its limitations and then see how agentic RAG changes the architecture by adding planning, tool use and iterative reasoning.
How Does Traditional RAG Work?
Traditional RAG follows a simple three-step pipeline: retrieve, augment and generate. The system converts the user’s question into an embedding and retrieves the most relevant passages from an indexed knowledge base. It then augments the prompt by combining those passages with the original question. Finally, the LLM generates an answer grounded in that retrieved context.
Every question follows the same path, whether it’s a simple fact lookup or a comparison requiring information from multiple documents. The pipeline runs once, from retrieval to generation and the model relies on whatever context the initial retrieval provides.
What Are the Limitations of Traditional RAG?
That single pass works well for straightforward questions where the relevant information exists in a small set of documents. The challenge appears when a question requires multiple steps of reasoning or information from different sources.
The pipeline also has limited ability to recover from poor retrieval results. If the retrieved passages are incomplete, outdated or only partially relevant, they are passed directly into the generation step. There is no built-in planning step to decompose complex questions, no reasoning loop to refine retrieval and no validation step to verify the final answer against the source material.
What Makes RAG “Agentic”?
Agentic RAG inserts an LLM-driven agent between the user and the retrieval machinery, and that agent treats answering as a task to plan rather than a pipeline to execute. Four capabilities separate it from the traditional setup:
Query planning and decomposition: The agent splits “How did Q3 revenue growth compare to last quarter’s guidance?” into two sub-questions, one about Q3 actuals and one about the guidance issued the quarter before, and answers each on its own.
Iterative retrieval: If the first pass returns thin results, the agent rephrases the query or tries a different source instead of settling for what came back.
Answer validation: Before responding, the agent checks the draft answer against the retrieved passages and flags claims that are not supported by the available sources.
Instead of following a fixed path from retrieval to generation, the agent introduces a feedback loop. After each retrieval step, it evaluates the results and decides whether the information is sufficient or whether it needs another search, or a refined query. Generation happens only after the agent has gathered enough evidence to answer the question.
Agentic RAG vs Traditional RAG: Side by Side
| Dimension | Traditional RAG | Agentic RAG |
| Query Handling | Question embedded as-is | Decomposed, rephrased and routed |
| Reasoning | None between retrieval and generation | Agent plans, evaluates results and decides next steps |
| Error Recovery | None; weak retrievals flow into the answer | Retries, reformulates or escalates when evidence is thin |
| Complexity and Cost | Few moving parts, one LLM call | More LLM calls, higher latency and token spend |
| Best Fit | Single-fact Q&A over a contained corpus | Multi-hop questions, cross-document comparisons, high-stakes answers |
Our take: the table can make agentic RAG look like the obvious winner, but the trade-off is not capability versus capability. It is capability versus complexity. Each step the agent adds means another LLM call, more latency and a system with more moving parts to maintain.
The right choice depends on the shape of your users’ questions. If most queries are simple lookups inside a well-structured knowledge base, traditional RAG is often faster and cheaper to maintain. If users regularly ask multi-step questions that require combining information across sources, an agentic approach can justify the additional complexity.
A Worked Example
Take the comparison question against a collection of indexed earnings reports: “How did Q3 revenue growth compare to last quarter’s guidance?”
A traditional pipeline embeds the full sentence and retrieves passages that resemble it overall. The top results may mention revenue growth and guidance in the same context, which often surfaces summary paragraphs or analyst commentary, while the specific guidance figure from the previous quarter’s report may not appear. The model then compares Q3 actuals against whichever guidance-related information made it into the retrieved context.
An agentic system plans first. It retrieves Q3 revenue growth from the Q3 report, then runs a second retrieval for the guidance published in the previous quarter’s report, using metadata such as document date to target the correct source. With both figures in hand and each tied to its source, the agent synthesizes the comparison and validates that each claim in the answer is supported by the retrieved passages. Two targeted retrievals replace one broad search.
When is Traditional RAG Enough?
A help-center bot answering “How do I reset my password?” over a few hundred product docs doesn’t need query decomposition or a validation loop. Each question maps to one passage, the corpus is contained and users care about speed. The traditional pipeline answers in a single model call, costs less per query and gives us fewer components to debug.
We’d frame it as a starting point rather than a lesser option. Start with the linear pipeline, watch the queries that fail and add agentic behavior when the failures cluster around multi-part questions the single pass can’t serve.
Progress Agentic RAG
Progress Agentic RAG treats this as a spectrum we can move along rather than a switch to flip. The Search configuration panel includes agentic steps such as query rephrasing, which rewrites questions for better retrieval, user-intent routing, which applies different configurations based on what the user is trying to accomplish, and semantic re-ranking, which reorders results by contextual relevance before they reach the model.
For the full agentic loop, the platform’s retrieval agents analyze a question, split it into sub-questions, and decide which sources to query for each part, including multiple Knowledge Boxes, SQL databases and internet search. Since all of this runs as a service, these capabilities can be enabled per configuration instead of requiring teams to rebuild their retrieval pipelines.
Wrap-Up
Traditional RAG runs retrieve, augment and generate once, in a straight line, which handles single-fact questions really well, but struggles with everything else. Agentic RAG hands that pipeline to an agent that plans retrievals, iterates on weak results and validates answers against sources, at the price of extra LLM calls and latency. Progress Agentic RAG lets us move between the two gradually, enabling query rephrasing, intent routing, re-ranking and retrieval agents on the same Knowledge Box as your needs grow. This enables more complex workflows that require synthesizing many different types of information.
FAQs
Do I need to rebuild my knowledge base to move from traditional to agentic RAG?
No. The agentic behavior lives in the retrieval and orchestration layer, not in the index. The same embedded, indexed content serves both approaches. In Progress Agentic RAG, we enable rephrasing, routing or retrieval agents on top of an existing Knowledge Box without re-ingesting anything.
What kinds of questions benefit most from agentic RAG?
Questions with more than one part: comparisons across documents or time periods, questions whose answers span several sources and vague questions that need rephrasing before retrieval can succeed. Answer validation also earns its cost anywhere a confidently wrong answer is expensive, such as financial or compliance queries.
For more details and to get started with Progress Agentic RAG, be sure to check out the following resources:
Hassan Djirdeh
Hassan is a senior front-end engineer and has helped build large production applications at-scale at organizations like Doordash, Instacart, and Shopify. Hassan is also a published author and course instructor where he’s helped thousands of students learn in-depth front-end engineering skills like React, Vue, TypeScript, and GraphQL.