Agentic RAG: A Next Generation RAG Architecture

Retrieval-augmented generation (RAG) has become a cornerstone technique for grounding large language models (LLMs) on external knowledge. However, traditional RAG pipelines are essentially static: an LLM retrieves documents from a single source and generates an answer, without the ability to reason about the retrieval process or adapt when one pass isn’t enough.

For example, a medical query like, What are the latest treatment options for Type 2 diabetes and how do they compare?, may require first retrieving up-to-date clinical trial results and then fetching guideline documents for comparison. A single retrieval pass would likely miss one of these layers, leaving the answer incomplete.

Agentic RAG is an emerging paradigm that embeds autonomous AI agents into the RAG pipeline. An agent is simply an AI system that can make decisions and take actions toward a goal, such as rephrasing a search query, calling an external tool (e.g., a calculator or database), or combining results from multiple sources before answering. This idea of agency, i.e., the ability to plan, act, and adapt, makes agentic RAG more powerful than standard RAG. By introducing reasoning loops, tool use, and even multiple cooperating agents, agentic RAG systems push beyond the limitations of standard RAG to handle more complex queries with greater adaptability.

In this newsletter, we’ll explore agentic RAG’s architecture and theoretical foundations, compare it with standard RAG, and guide on implementing an agentic RAG system, complete with example code and diagrams.

From RAG to agentic RAG#

A traditional RAG system consists of two main components:  

• A retriever (usually an embedding-based vector search over a knowledge base)  

• A generator (an LLM)

When a user query arrives, the retriever finds relevant documents from a vector database and passes them (often as additional context) to the LLM, producing the answer. This simple, retrieve-then-generate workflow augments the LLM’s prompt with up-to-date information, reducing hallucinations and domain misses. However, the LLM in vanilla RAG has no control over retrieval beyond the initial query. For example, if a user asks, Who won the Nobel Prize in Physics and what was their research about?, the retriever may only return the winner’s name from one document. The LLM cannot autonomously perform a second retrieval to fetch details about the research itself, leaving the answer incomplete. In essence, standard RAG is reactive and limited: it performs a single lookup per query and cannot adjust if the first retrieval is insufficient.

Agentic RAG architecture #

Agentic RAG augments this pipeline by inserting an intelligent agent (or even a team of agents) into the loop. Instead of a static retrieval step, the agent, which is typically powered by an LLM with tool-use capabilities, actively orchestrates the retrieval and reasoning process. The agent can analyze the query and decide which knowledge source(s) or tool(s) to use, break complex questions into sub-queries, perform multiple retrievals iteratively, and post-process or validate the results before final answer generation. In other words, the agent introduces dynamic control flow into the RAG pipeline, turning it from a fixed sequence into a flexible loop of planning, retrieval, and generation.

Under the hood, an agentic RAG system often uses an orchestrator agent (a central reasoning LLM) to call various tools such as a vector DB search tool, a web search API, calculators, or other domain-specific services. In some implementations, this orchestrator delegates retrieval to a specialized retriever agent (as shown in the illustration), while in others, the orchestrator directly manages retrieval. The division depends on how modular or specialized the system is designed.

In a simple case, this agent acts as a routing agent, deciding which of multiple knowledge bases to query based on the question type. For example, a customer-support bot might route billing-related questions to a finance database but send technical troubleshooting queries to a product documentation index.

More advanced implementations use multiple specialized agents working together (a multi-agent architecture): one agent might plan and decompose the task, others execute searches in parallel on different sources, and another might aggregate and verify the answers. For instance, in a healthcare system, a planner agent could break down the query, What are the latest treatment options for diabetes and how do they compare?, into subtasks: one retriever agent fetches clinical trial results, another queries medical guidelines, and a synthesis agent merges the findings into a coherent summary.

In an enterprise setting, a market research assistant might split a query like, How is AI adoption trending in the financial sector?, into sub-queries: one agent retrieves recent analyst reports, another scans industry news, and another compiles survey data. The orchestrator then integrates these diverse inputs into a strategic briefing.

In such designs, the orchestrator agent doesn’t always do the retrieval itself. It can delegate that step to a specialized retriever agent (like in the illustration above), while other agents focus on reasoning, verification, or synthesis. This is analogous to a team of specialists: e.g., one agent knows to search a technical docs database while another queries a customer support FAQs, and a coordinator agent merges their findings. The agentic RAG thus generalizes the RAG concept beyond a single retrieval from a single source, as it can tap into heterogeneous data and handle non-linear workflows.

Key innovations #

Compared to standard RAG, agentic RAG brings several innovations in flexibility and intelligence:

• Multiple knowledge sources and tools: Traditional RAG connects an LLM to one knowledge base (often one vector index). Agentic RAG can integrate multiple databases and even external APIs or services. The agent can choose the appropriate tool or source for each query or sub-task. For example, a query might require both a vector search in documentation and a web search. An agentic system can do both and combine the results in one answer.

• Adaptive, multi-step retrieval: Agentic agents perform reasoned, iterative retrieval instead of a one-shot retrieve. They can reformulate queries or do multi-hop searching: e.g., first retrieve a relevant document, then realize a follow-up query is needed, and retrieve again. The agent thinks in a loop, using the outcome of each retrieval (observation) to decide the next step. This enables tackling complex questions that require combining information or drilling down through intermediate questions.

• Autonomous decision-making: The agent introduces a degree of autonomy: it can decide what actions to take (which tool to use, whether to stop or continue searching, etc.) without explicit human instruction at each step. This moves the pipeline from a hard-coded sequence to an intelligent controller that adapts to the query’s needs on the fly. It’s the difference between a search engine that only returns exactly what you ask for and an AI assistant that figures out what it needs to do to answer your question.

• Maintaining context and dialog: In multi-turn interactions (conversations), an agentic RAG system can maintain context over turns, ask clarifying questions to the user if needed, and ensure consistency. For instance, Amazon’s agentic Q&A system can keep track of the ongoing conversation and improve response quality by retrying queries or rephrasing them when the retrieved information is insufficient or ambiguous.

• Quality control and self-optimization: Agents can act as critics or verifiers. An agentic RAG might include a step where an agent evaluates the draft answer against the source content, checks for missing info, or flags uncertainty, then triggers another retrieval if needed. This feedback loop allows iterative refinement. Over time, some systems learn from feedback (storing successful query strategies, caching results, etc.), gradually improving performance.

It’s important to note that agentic RAG is a broad concept encompassing a spectrum of designs. On one end, it might be a single LLM with the ability to call a search tool (essentially a self-queried RAG). Conversely, it could be a complex multi-agent system with hierarchies: e.g., a top-level manager agent delegates subtasks to worker agents (each might handle a specific modality or database). As explored in recent research, the extreme of this idea includes graph-based agentic systems where agents form networks to solve queries, or Agentic Document Workflows (ADW) that integrate agents into document processing pipelines. Regardless of specific flavor, the unifying theme is that agency is embedded in the retrieval-augmented generation (RAG) loop.

Why agents use loops to think and act#

Now that we’ve introduced agents, let’s look at how they think. At the heart of agentic RAG is the idea of reasoning loops, a process where the agent doesn’t just answer once, but pauses, plans, and acts step by step. Fundamentally, this involves giving the LLM capabilities beyond one-shot prediction: memory, planning, tool use, and self-reflection.

Agentic AI basics #

An AI agent is an AI system that can autonomously determine and execute actions to achieve a goal. Modern LLM-based agents typically have three key characteristics: 

• Some form of memory (short-term scratchpadA scratchpad is where the model “writes down” partial steps (like calculations, sub-queries, or logical reasoning) during a task. It’s short-term because it only lasts for the current reasoning loop or session, unlike a knowledge base which stores information persistently. or longer-term knowledge store) to remember intermediate results or past interactions 

• The ability to do step-by-step reasoning and decision-making (e.g., break a task into sub-tasks, choose among alternative strategies); 

• Tool-use via function calls or APIs to interact with external systems. 

By integrating these into RAG, we allow the LLM to manage the retrieval process rather than passively accept retrieved text.

ReAct and reasoning loops #

One influential paradigm enabling agentic behavior in LLMs is the ReAct framework (Reason+Act). In ReAct, the LLM is prompted to generate an answer and a sequence of thoughts and actions.

For example, given a question, the model might output a thought like, I should search the database for X, then an action instructing a search tool. The result from the tool (observation) is fed back, and the model’s next thought can build on it, and so on. This loop continues until the model decides it can produce a final answer. The ReAct pattern effectively creates a reasoning loop where the LLM can iteratively retrieve information and update its plan. Agentic RAG often uses variations of this approach, allowing the system to refine queries, correct itself, and dig deeper based on what it finds.

For instance, imagine a user asking, “Plan me a three-day trip to Tokyo, including cultural activities and local food.” A standard RAG system might only pull a single list of attractions. However, an agentic system could retrieve flight and hotel information, query restaurant databases, and then fetch cultural event listings, adjusting its plan step by step until it produces a complete itinerary.

Note: Beyond basic ReAct, researchers have proposed enhanced agentic patterns, some of which have been discussed in the following course: 

Function calls vs. frameworks#

The introduction of agentic capabilities transforms the RAG workflow into an intelligent, interactive process. An agentic RAG can emulate aspects of human problem-solving: asking clarifying questions, using reference materials intelligently, double-checking answers, and orchestrating a strategy to solve complex queries. At this point, we’ve seen the core ideas. Now let’s look at two practical ways these are put into practice.

LLM function calling#

The first approach is LLM function calling. Several LLM providers now allow models to output a structured function call which the system can execute, returning the result to the model. This provides a lightweight way to add agent-like behavior. For example, you might define a simple “search” function that queries your vector DB and let the model call it whenever needed. This lowers the barrier to adding agentic behavior but remains limited: the model can only trigger predefined functions one at a time, with little flexibility in orchestrating multi-step plans.

Using dedicated frameworks#

The second approach is using dedicated frameworks like LangChain or LlamaIndex. These libraries go beyond function calling by providing richer templates and orchestration layers. They support advanced patterns such as multi-step reasoning loops, custom memory, or even multi-agent collaboration. This makes them better suited for building scalable, production-ready agentic RAG systems.

Choosing the right level of RAG and agency#

Agentic RAG is powerful, but it isn’t always necessary. The right setup depends on the complexity of the query, the diversity of data sources, and the trade-off between speed and accuracy. To make this concrete, let’s look at scenarios where different levels of RAG and agency are appropriate.

In the simplest cases, traditional RAG is enough. If a customer asks a fact-based question like:

A retriever and generator can handle it. The system pulls a single, relevant piece of information and generates an answer. Adding agents here would only slow things down without improving accuracy.

There are also situations where a simple agent suffices, without RAG at all. For instance, when a user asks a chatbot to calculate a compound interest rate or convert a block of text into JSON, there’s no need to fetch from an external knowledge base. The model just needs reasoning and tool-use capabilities. Here, a lightweight agent that calls a calculator or formatting tool is more efficient than building a full RAG pipeline.

More demanding tasks call for RAG plus a single agent. Imagine a legal researcher asking:

A basic RAG pipeline could retrieve documents, but a reasoning agent ensures queries are reformulated if retrieval quality is poor and that the answer is cross-checked against the sources. One agent orchestrating the process is enough to boost reliability without excessive complexity.

Finally, some domains demand multi-agent RAG architectures. Take health care—a doctor might ask:

No single retrieval pass will cover this. One agent may specialize in guidelines, another in trial databases, and another in summarizing research papers. A coordinator agent merges their outputs. The system essentially mimics a team of experts working together, and while it is more resource-intensive, it’s indispensable for high-stakes, multi-source reasoning.

Performance, capabilities, and limitations compared to standard RAG#

Agentic RAG delivers clear advantages over standard RAG, but at the cost of added complexity.

Benefits:

• Better accuracy and freshness: Agents can reformulate queries, verify answers, and pull in real-time data, reducing hallucinations.

• Greater adaptability: Able to decompose complex, multi-step questions and refine answers iteratively.

• Scalability and multimodality: Can orchestrate across multiple databases, APIs, or even vision tools—handling diverse data and tasks.

Trade-offs:

• Higher cost and latency: Multi-step reasoning and tool calls mean slower responses and more compute.

• Engineering complexity: More moving parts (agents, memory, tools) increase failure points and debugging effort.

• Not hallucination-proof: While improved, agents can still make poor decisions or misinterpret sources. Guardrails and oversight remain essential.

Conclusion#

Agentic RAG evolves retrieval-augmented generation (RAG) by integrating knowledge grounding with AI agent decision-making. Instead of a rigid retrieve-and-read pipeline, it enables a loop of reasoning, acting, and retrieving, and hence, producing systems that can handle complex information tasks with greater adaptability.

But as we’ve seen, not every problem requires the full machinery of multi-agent RAG. For simple fact lookups, a traditional RAG pipeline remains the fastest and most efficient choice. For lightweight reasoning without retrieval, a single agent can do the job. When retrieval is necessary but tasks are moderately complex, a RAG + single agent setup balances accuracy with cost. Only in high-stakes, multi-source scenarios do we truly need the full power of multi-agent RAG, where specialized agents coordinate like a team of experts.

In essence, agentic RAG represents a shift from one-size-fits-all retrieval to a layered ecosystem of intelligence, where different levels of agency are applied thoughtfully. The challenge ahead is not just building more powerful agents, but knowing when to use them. Those who master this balance will shape the next generation of AI assistants, applications, and enterprises.

Interested to learn more about basic RAG and agentic libraries? Check out the following courses: