Quick Answer: Traditional RAG retrieves context once and then answers, whereas agentic RAG lets an agent decide what to search, where and how many times. The agent handles multi-step, cross-source questions better, but Microsoft's architecture guide puts a three-to-five-tool-call request at 8 to 15 seconds versus 2 to 3. In the agentic RAG vs RAG choice, keep single-pass retrieval for simple lookups.
Most RAG systems in production run a fixed pipeline: embed the question, search one index, put the top chunks in the prompt, generate. That holds until users ask questions that need two lookups, a comparison, or data the index never saw.
This guide compares agentic RAG vs RAG for teams with a knowledge base behind a chat or support product, and ends with a test to run on your own question log.
What is the difference between agentic RAG and traditional RAG?
The difference is who controls retrieval. Traditional RAG runs the search you designed, once, then answers. The agentic version hands retrieval to the language model as a tool: the model calls it when it decides to, checks the results, and repeats until it has enough context.
Microsoft's architecture guide describes the standard pipeline as a fixed sequence in which the orchestrator never decides whether to search, which index to query or how many steps to take. That is the whole split in agentic RAG vs traditional RAG: the agent makes those decisions at run time.
| Dimension | Traditional (single pass) | Agentic (agent loop) |
|---|---|---|
| Control loop | Fixed at design time: search, build prompt, generate | Reasoning loop: plan, call a tool, evaluate, repeat |
| Retrieval passes | One per question | One to many, decided at run time |
| Sources | Usually one index | Several indexes, APIs, databases or web search |
| Latency | About 2 to 3 seconds in Microsoft's example | About 8 to 15 seconds with three to five tool calls |
| Token use | One generation plus retrieved chunks | Every reasoning step adds input and output tokens |
| Failure modes | Wrong chunks when the first search misses | Wrong tool choice, loops that don't converge |
| Best for | Single-hop lookups in stable documentation | Multi-hop, comparison and cross-source questions |
Latency figures are Microsoft's illustrative ranges (guide updated 30 June 2026), not product benchmarks.
How does an agent decide what to retrieve?
An agent decides from three inputs: the user's question, its instructions, and the descriptions of its retrieval tools. It picks a tool, reads the results, then searches again or answers.
That decision breaks into four moves:
- Query planning. A question comparing two regions becomes two lookups plus a comparison step.
- Source selection. With separate tools for product docs, order history and runbooks, the model picks the one that fits. Microsoft advises keeping the total under 20 tools.
- Iterative retrieval. If the first pass comes back thin, the agent rewrites the query or adds a filter and searches again.
- Self-check. Before answering, the agent judges whether the evidence covers the question. A 2025 survey on arXiv names reflection, planning, tool use and multi-agent collaboration as the core design patterns.
Most of the agent's judgement comes from the tool descriptions you write, so make each one name its data source. Inside each tool, retrieval is ordinary RAG engineering, which is why choosing among the best vector databases for RAG still matters. GraphRAG is a different change: it alters what gets indexed, not who controls the loop.
When does traditional RAG answer well enough?
Traditional RAG is enough when a question maps to one search against one index: FAQs, policy lookups, product specs and how-to steps in stable documentation. Microsoft's guide agrees: if a single search resolves the query, standard RAG is the better fit. Teams that would rather adapt the model than the retrieval step can weigh that route in how to train ChatGPT on your own data.
It also wins where latency is part of the product, such as a support widget.
Product teams choosing between a one-click knowledge base tool and a custom RAG pipeline face the same question from another side. If most questions are lookups, a single pass is enough. If many are multi-step, check whether the tool exposes retrieval as an API an agent can call. The build-or-buy trade-off is covered in knowledge base tool vs custom RAG pipeline.
What does agentic RAG add in latency and token use?
Every reasoning step is another model call, and every tool call adds a search round trip plus the text it returns. In Microsoft's example, three to five tool calls turn a 2-to-3-second answer into roughly 8 to 15 seconds.
Tokens grow the same way, because each step usually carries the question, the instructions and everything retrieved so far. IBM's explainer notes that an agentic system usually means paying for more tokens.
You can cap both:
- Set an iteration limit. Microsoft calls a 5-to-10 tool-call cap per request typical.
- Track a token budget per request and stop when it's spent.
- Write stop rules into the instructions, such as "answer once two sources agree".
- Return three to five results per tool call to start, then tune.
- Route by difficulty, so only questions that need the loop enter it.
- Add a timeout and a fallback answer.
How do you evaluate an agentic RAG system?
Evaluate it as retrieval plus a controller. Measure answer accuracy and retrieval recall as for any RAG system, then add tool-selection accuracy, steps per answer, latency and cost per request, and read the traces.
| Metric | What it tells you | How to measure it |
|---|---|---|
| Answer accuracy | Is the answer right and grounded? | Labeled test set; each claim traces to a retrieved passage |
| Retrieval recall | Did the right evidence come back? | Recall@k against marked passages |
| Tool-selection accuracy | Does the agent pick the right source? | Expected tool per test question vs actual calls |
| Steps per answer | Is the loop efficient? | Tool calls per request |
| Latency | Can users wait for it? | p95 end to end, split by reasoning and tool time |
| Cost per request | Is the quality gain worth it? | Model and search calls vs a single-pass baseline |
| Permission correctness | Can a user see a document they shouldn't? | Test questions asked as users with different access |
Log every tool call and its results, then read a sample of failures by hand. If recall is poor in both setups, the agent isn't the problem: look upstream at parsing and RAG chunking strategies.
Which knowledge-base questions need agentic retrieval?
Questions need agentic retrieval when one search can't hold the answer: multi-hop questions, questions spanning sources, comparisons, "why" questions that need several documents, and questions that end in an action.
Run a question-log test on your own traffic:
- Export 100 to 200 recent questions from chat logs, tickets or site search.
- Tag each one: lookup, multi-hop, cross-source, comparison, why, or action.
- Run the sample through a single-pass pipeline and score each answer.
- Sort the failures by tag. Failures clustered outside the lookup tag mark your candidates.
- Count the share. If multi-step questions are a small slice, route only those through an agent, and keep the sample as your test set.
If you're still comparing AI knowledge base builders for chat and support, run the same tagged sample through each shortlisted option.
What mistakes should you avoid when adding agents to RAG?
The expensive mistakes are adding a loop where one search would do, letting it run unbounded, and adding agents before you can measure retrieval.
- Agent loops for simple FAQs. Lookups get slower and costlier with no accuracy gain.
- No loop cap or token budget. One confused request can make dozens of calls.
- No retrieval evals first. Without a baseline you can't tell whether the agent or the index failed.
- Ignoring permissions per source. Each tool is an attack surface; apply least-privilege access per source and never return credentials in tool output.
- Vague or overlapping tool descriptions. The model can't choose between tools it can't tell apart.
How Origins AI Velocity AI Suite supplies the retrieval layer
Origins AI (originshq.com) builds Origins AI Velocity AI Suite, a modular enterprise AI suite that supplies the retrieval layer for a company knowledge base, callable from a RAG pipeline or an agent. Its product page describes a knowledge foundation (data intake, document intelligence, retrieval indexing) and an AI core with a retrieval engine, model orchestration and bring-your-own model APIs.
The company reports 1,900+ data sources and 91+ document formats, and lists Pinecone, Chroma and Weaviate among supported vector databases. The page doesn't describe a built-in agent reasoning loop, so scope the loop, its caps and stop rules as engineering work in a pilot.
The suite is deployed in your environment by an implementation team. In on-premise and air-gapped modes with self-hosted models, documents and queries stay inside your network; if you connect a hosted model instead, retrieved chunks go to its provider with each prompt, and an agent loop sends more of them per question.
Talk to an engineer
Not sure your knowledge base needs agentic retrieval? Run the question-log test above, then book a call and bring a sample of real user questions. An Origins AI engineer will walk through which ones need an agent. Book a call.
Written by Apoorva Kumar, Co-Founder & CEO, Origins AI.