What Is Agentic RAG?

Agentic RAG—Explained

As enterprises increasingly adopt generative AI (GenAI), they face a persistent challenge—how to support AI outputs that are accurate, relevant and trustworthy. Large language models (LLMs), while powerful, are inherently limited by their static training data. This often leads to outdated information, hallucinated responses and a lack of transparency, which is especially problematic for highly regulated industries or mission-critical applications.

Retrieval-Augmented Generation (RAG) addresses this gap by connecting LLMs to external knowledge sources, such as internal documents, databases, APIs and web content. This enables AI systems to generate responses grounded in real-time, verifiable data rather than relying solely on pretrained knowledge.

For organizations, RAG technology unlocks several strategic advantages:

• Improved Accuracy: Responses are backed by enterprise data, which reduces hallucinations and misinformation.

• Faster Decision-Making: Employees can access precise answers from vast unstructured data (residing in documents, video, audio, text, etc.) instantly.

• Operational Efficiency: RAG can help to automate complex tasks like contract analysis, claims processing and customer support.

• Compliance and Governance: These solutions provide traceability and auditability—essential for the legal, financial and healthcare sectors.

Agentic RAG is an advanced AI architecture that combines the power of RAG with autonomous AI agents. Unlike traditional RAG systems, which statically retrieve and generate responses, agentic RAG introduces dynamic, goal-oriented agents that can reason, plan and act across complex workflows. These agents orchestrate retrieval strategies, validate outputs and adapt responses in real time—enabling more accurate, trustworthy and context-aware AI solutions.

Agentic RAG vs. Traditional RAG

While RAG has become a foundational architecture for enterprise AI as organizational needs grow more complex, traditional RAG systems often fall short. As mentioned previously, agentic RAG introduces autonomous AI agents into the RAG pipeline to deliver dynamic, adaptive and trustworthy AI experiences.

Differences

Traditional RAG technology uses static retrieval methods—typically keyword-based or dense vector search—to fetch documents. Retrieval logic, in this case, is predefined and does not adapt to query complexity. While effective for straightforward queries, this approach lacks the flexibility to adapt to varying query types or complexities.

In contrast, agentic RAG employs AI agents that can dynamically select retrieval strategies based on the query type, context and domain. Agents can choose between semantic search, structured database queries, web search or even recommendation engines. This adaptive logic allows agentic RAG to handle a broader range of tasks with higher precision and relevance.

Legacy RAG systems follow a linear workflow: ingest → retrieve → generate. Once a user submits a query, the system performs a single retrieval pass—typically using a keyword or vector search—and feeds the retrieved documents directly into the language model for response generation. This pipeline is static, meaning it doesn’t adapt based on the complexity of the query or the quality of the retrieved context. In addition, there’s no mechanism for iterative refinement or validation.

On the other hand, agentic RAG systems introduce multi-step, agent-driven workflows that are dynamic and context-aware. Instead of single retrieval pass, autonomous agents can evaluate the query, select appropriate tools and orchestrate multiple retrieval strategies, such as semantic search, structured database queries or real-time web access. These agents can iterate over the retrieval process, refine context, validate sources and even re-query based on intermediate results. This adaptive workflow allows agentic RAG to handle complex, multi-domain queries with precision, making it ideal for use cases like legal analysis, healthcare diagnostics and financial forecasting, where accuracy and traceability are paramount.

Traditional RAG systems enhance LLMs by retrieving external documents to ground responses, but they often fall short in delivering full transparency. These systems typically retrieve context in fixed-size chunks without semantic awareness, which can fragment meaning and reduce answer quality. Moreover, traditional RAG lacks built-in mechanisms to validate retrieved information or explain how a response was generated. Citations may be included but they’re often generic or incomplete, making it difficult for users to verify the provenance of an answer.

Agentic RAG systems are designed to build trust through transparency and verifiability. Autonomous agents not only retrieve information, but also validate it, log their decision-making steps and provide source-level citations for every answer. These systems use semantic chunking and smart segmentation to preserve meaning, resulting in retrieved context that is coherent and relevant. For compliance officers, legal teams and risk managers, this level of transparency transforms AI into a strategic asset.

Agents in AI Systems

In agentic RAG systems, agents are autonomous AI entities designed to reason, plan and act within a RAG pipeline. Each agent is powered by an LLM and equipped with tools, memory and planning capabilities. These agents can interpret user queries, determine the best retrieval strategy, interact with external data sources and refine responses iteratively. Their ability to adapt based on context and feedback makes them essential for handling complex, multi-domain queries with precision and relevance.

Why Agents Are Important for Retrieval

Agents in agentic RAG systems operate within a modular architecture governed by an orchestration layer. This layer manages the “Thought-Action-Observation” cycle: agents think (reason about the query); act (retrieve or process data using tools); and observe (reflect on results to decide next steps). For example, a coordinating agent may receive a query and delegate tasks to specialized agents—one for structured data (SQL); another for semantic search (vector databases); and another for real-time web data. Each agent uses its domain-specific tools to retrieve relevant information, which is then synthesized by the LLM into a coherent, context-aware response.

Agentic RAG Architecture

Ingestion

In agentic RAG, ingestion is more than just uploading documents; it’s the foundation for intelligence retrieval. Agents assist in transforming unstructured content (i.e., PDFs, videos, audio, etc.) into structured, queryable knowledge. This includes semantic chunking, entity extraction, labeling and metadata enrichment. Agents can also apply access controls and sensitivity tagging, facilitating that downstream retrieval respects governance policies.

Retrieval

Retrieval in agentic RAG is dynamic and agent-driven. Instead of relying on a single static method, agents evaluate the query and select the most appropriate retrieval strategy: semantic vector search, structured database queries, real-time web search or API calls. In multi-agent setups, specialized agents handle different data domains (e.g., SQL, PDFs, web, etc.) and a coordinating agent orchestrates their collaboration.

Augmentation

Once data is retrieved, augmentation processes it to extract the most relevant segments and align them with the query. This may involve summarization, filtering or contextual re-ranking. Agents can iteratively refine the retrieved content, discard irrelevant information and enhance semantic coherence. This step enables that the final input to the LLM is not just a dump of documents, but a curated, high-quality context that improves the accuracy and relevance of generated responses.