Agentic RAG 101: The intelligent decision flow behind reliable agentic RAG systems
Over the past few years, Retrieval-Augmented Generation (RAG) has become one of the go-to techniques for grounding large language AI models in trustworthy, domain-specific information. But the AI conversation has shifted, with the spotlight moving from GenAI toward autonomous agents and data orchestration. Systems that don’t just answer questions, but decide how to get the best answer in the first place. It’s in this context that “agentic RAG” emerges: an evolution of the classic RAG pipeline into something more adaptive, self-correcting, and context-aware.
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Traditional Retrieval-Augmented Generation (RAG) pipelines run in a straight line: they retrieve documents first, then generate a response. But a search isn’t always necessary, nor does the input always enable a correct answer. What if your system could decide when to search, score the retrieved information, and rewrite the query when the first pass misses the mark?
That’s where agentic RAG comes in: a more intelligent approach in which autonomous agents make decisions throughout the retrieval and generation process. Think of it as upgrading a basic tool into an intelligent and self-aware assistant that can reason about its own performance and adapt on the fly.
In this blogpost, we’ll walk you through an agentic RAG system. Our tech team at Studio Fledge delves into the code in their blog, so make sure to head to Medium if you want to build your own agentic RAG system. The blogpost you’re currently reading, however, will focus on the architecture and the reasoning behind each decision our agentic RAG system makes.
What’s “agentic” about this RAG system?
The core difference between traditional RAG and agentic RAG is the decision-making process. Instead of a fixed “retrieve → answer” line, the system introduces checkpoints where it chooses what to do next based on quality. Several key capabilities demonstrate this agency:
- Intelligent query routing: A supervisor agent inspects the incoming question and decides whether to search (use retrieval) at all, or respond right away based on existing knowledge. Basic queries like “What is Python?” wouldn’t need a search, while targeted, technical questions do trigger comprehensive document retrieval.
- Self-correcting queries: If the first retrieval pass doesn’t come up with relevant documents, the system doesn’t settle. It rephrases the query with alternative terminology or a different angle, until quality improves or it concludes that the knowledge base doesn’t contain the answer. Our system sets the limits at three passes to push for better results while keeping it sensible.
- Multi-stage validation: The quality control process has two tiers. The system first checks whether retrieved passages are genuinely relevant, then verifies that the generated answer is grounded in those passages and actually addresses the user’s request.
- Graceful degradation: When the system isn’t confident or the information provided doesn’t suffice, the system communicates uncertainty instead of improvising responses. Honesty is a feature, not a failure mode.
Architecture overview: the intelligent decision flow
In contrast with traditional RAG pipelines, agentic RAG follows a sophisticated decision-making process that can adapt based on quality assessments at each node:
Supervise → Retrieve documents → Grade documents → Generate → Grade answer → Wrap up
Each step can redirect the flow. For example, if document grading finds poor relevance, the system routes back to query reformulation and tries again. If the generated answer isn’t adequately supported or isn’t useful enough, it loops to improve inputs rather than providing a mediocre response.
How the system works: a step by step deep-dive
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- Supervise: deciding whether to search
The system starts by asking a deceptively powerful question: Do we need retrieval at all? Many queries are definitional or broadly known to the model. In those cases, skipping retrieval reduces cost, latency, and noise. When the question is specific (procedures, constraints, product versions), the supervisor invokes retrieval and passes along the query.
- Retrieve: find answers
The system searches documents to find answers to the query at hand. Semantic relevance is key. After it gathers a healthy set of candidates, the system then re-ranks and compresses them to a compact, high-signal context window.
- Grade documents: quality check
Before we ever draft an answer, a grader evaluates whether the retrieved snippets are actually about the question. It’s lenient enough to keep useful context, but strict enough to reject off-topic passages that would mislead generation. If the grader isn’t satisfied, the system doesn’t force it. It routes to query improvement.
- Rephrase the query: learning from fails
When relevance is low, the system proposes a better search: adjusted terminology and synonyms, altered scope, or a flipped angle. Crucially, it remembers past attempts to avoid cycling. We cap retries (e.g., three) to balance persistence with pragmatism.
- Generate: grounded, helpful answers
The system only writes an answer when the context clears the bar. The generator is asked to be specific, actionable, and grounded. If the context is insufficient, it’s encouraged to say so.
- Grade the answer: truth and usefulness
A second grader evaluates two things:
- Grounding: is the answer supported by the retrieved facts (or does it admit uncertainty appropriately)?
- Usefulness: does the response address the question with sufficient detail?
If it fails either of these tests, the system loops. It will try to either improve retrieval or sharpen the query, or (when appropriate), it will respond with explicitly acknowledged uncertainty.
- Wrap up (or degrade gracefully)
Strong, grounded answers are delivered as-is. If support is thin after multiple attempts, the system explains the limitation and shares the best available context. After all, we believe users prefer a clear boundary to a confident hallucination.
Agentic RAG turns a brittle line into a thoughtful loop. It thinks about when to search, tests what it found, improves how it searches, and is honest about what it knows. The result is not just fewer hallucinations, but a system that behaves like a careful partner: fast when it can be, cautious when it must be, and always optimizing for a useful, grounded answer.
If you want to see the full code and implementation details behind this architecture, check out Studio Fledge’s step by step coding guide and our GitHub repo. This article gives you the why and the how at the system level, so you can decide where agentic RAG fits into your stack before you ever touch a dependency list.
