What is Agentic RAG and how does it compare to traditional RAG?

Traditional RAG operates as a deterministic, blind pipeline: a user submits a prompt, the system embeds it, queries a vector database for top-k chunks, concatenates those chunks into the LLM context, and generates an answer. It is a single, unreflective pass.

The catastrophic flaw in this architecture is that it assumes the user query was phrased optimally, that the embedding distance captured the true semantic intent, and that the retrieved chunks actually contain the answer. If the retrieved chunks are irrelevant or incomplete, the model is trapped in a dilemma: either admit it does not know or generate a hallucinated blend of half-truths.

In the real world, human questions are messy, ambiguous, and frequently multi-layered. When an engineer asks "Why did our Kubernetes ingress fail after the 2.4 deployment?", a single vector search cannot pull the root cause because the answer is scattered across deployment manifests, git logs, and pod error outputs.

Agentic RAG converts passive retrieval into an active, goal-driven reasoning loop. Instead of immediately dumping raw search results into an answer prompt, the system equips an LLM agent with tools — such as vector search, SQL queries, document summarizers, and query rewriters — and allows the agent to orchestrate the research process.

When a question arrives, the agent analyzes whether it even requires retrieval. If retrieval is needed, the agent plans a sequence of actions: it rewrites complex prompts into targeted search terms, inspects the retrieved results for relevance, and determines whether more data is necessary before synthesizing a final response.

• Query Planning & Routing: The agent decides whether to route the question to a vector database, a SQL database, an API, or answer directly from internal model weights.
• Sub-Query Decomposition: Complex comparative questions are broken down into distinct independent queries that run in parallel or sequentially.
• Self-Reflection & Critique: The agent inspects retrieved chunks and assigns a confidence score; if the chunks fail to answer the question, it reforms the query.
• Corrective Fallbacks: If internal retrieval yields nothing of value, the agent autonomously falls back to secondary databases or authoritative web search.

Within the agentic paradigm, several specialized architectural frameworks have emerged to handle specific edge cases in enterprise production systems.

Self-RAG (Self-Reflective RAG) trains models to emit special reflection tokens that evaluate whether retrieval is necessary, whether the retrieved passages are relevant, and whether the final generated output is supported by the cited passages.

Corrective RAG (CRAG) implements an automated retrieval evaluator. If the evaluator flags retrieved documents as ambiguous or incorrect, CRAG discards the noise and executes a lightweight search query across external sources, guaranteeing that only high-confidence data reaches the generator.

Adaptive RAG dynamically classifies query complexity: simple factual questions are answered without retrieval or with single-shot search, while intricate analytical queries trigger full multi-step agentic workflows, optimizing both API cost and user latency.

Building an agentic RAG pipeline does not require complex frameworks. At its core, it is an LLM with function calling capabilities connected to a small set of well-defined retrieval tools.

Define a `retrieve_documents(query: string, category: string)` tool that interacts with your existing PostgreSQL pgvector or Qdrant index. Then, provide the agent with a system prompt that mandates self-checking: instructed to verify whether retrieved text directly answers the question before outputting a conclusion.

Start with a two-step pattern: generate query variations, evaluate the returned search candidates, and only generate an answer once the relevance score crosses a predefined threshold.

• Implement query rewriting to turn conversational language into keyword-dense search terms
• Add a lightweight re-ranking model (such as Cohere Rerank or BGE-Reranker) before the agent inspects chunks
• Set maximum iteration limits (e.g. max 3 retrieval loops) to prevent infinite searching and cost runaway
• Log all intermediate thought steps and tool invocations to track retrieval quality and catch agent loops