Agentic RAG: Definition, Best Practices, & Tools

As language models take on more complex roles in production systems, the expectations around adaptability, autonomy, and reasoning are evolving. Traditional approaches like retrieval augmented generation (RAG) have been effective for grounding LLM outputs, but they fall short when it comes to handling dynamic, multi-step tasks.

This is where agentic RAG enters the picture. By introducing AI agents into the RAG workflow, teams can build systems that don’t just retrieve and generate, but also plan, reason, and act. The result is a new class of LLM-powered applications that are more responsive, context-aware, and capable of navigating real-world complexity.

In this blog post, we explore what makes agentic RAG a critical evolution in the LLM stack. We’ll break down how it differs from traditional RAG, why it matters for modern AI use cases, and what it takes to build and operationalize these systems effectively. Whether you're wondering what agentic behavior looks like in practice or how to scale multi-agentic RAG workflows, this guide will give you the clarity and tooling you need to get started.

Let’s dive in.

Understanding Agentic RAG

As LLMs take on more dynamic roles in production environments, the architecture behind how they retrieve and reason over information is evolving. One of the most important shifts in this space is the move from static RAG pipelines to more flexible, agentic systems.

What is Agentic RAG?

Retrieval-Augmented Generation pairs a language model with a vector database, allowing it to ground its responses in retrieved content. But classic RAG is limited since it retrieves once, generates once, and has no sense of history or intent.

Credits: Leeway Hertz

Agentic RAG changes that. In this setup, the system doesn’t just respond; it reasons, plans, and adapts. It brings in agentic behaviors like multi-step planning, memory, tool use, and even basic forms of self-reflection. Rather than executing a single retrieval step, an agentic system can decide what to retrieve, when, and how to act on it, often in a loop.

A simple example: traditional RAG is like asking a librarian for one book on a topic. Agentic RAG is like giving a research assistant a goal; they’ll search, summarize, double-check, and even shift direction if needed.

In more complex workflows, multi-agentic RAG setups enable multi-agent systems, where different agents specialize in retrieval, RAG evaluation, or decision-making. This coordination unlocks new levels of flexibility for handling nuanced queries or long-running tasks.

What Agents Add to the Mix

When we introduce agents into the RAG pipeline, we unlock a whole new level of capability. Agents bring the decision-making ability to break down complex queries and execute tasks in an organized, iterative manner.

Planning & Decomposition

One of the key features of agentic RAG is the ability to break complex queries into manageable steps. Instead of a single-query approach, agents decompose tasks based on user intent, prioritizing steps for optimal completion. For example, if the user asks for a detailed market analysis, an agent can plan a multi-step process. This would start with data retrieval, followed by analysis, and ending with synthesis into a coherent summary. This form of orchestration is essential for tasks requiring nuanced understanding and long-term engagement.

Tool Use

A hallmark of agentic behavior is the ability to invoke external tools. Whether it’s making API calls, running calculations, or triggering workflows, agents act as a bridge between multimodality and LLM-based reasoning. For instance, an agent may retrieve relevant documents from a vector database, then use a calculation tool to process data, and finally call an API to fetch real-time market data. This seamless integration of tools boosts the system’s flexibility and scalability, enabling it to handle a variety of workflows with ease.

Memory & Adaptivity

Another powerful feature is memory, both short-term memory and long-term memory. Agents can store context across steps, ensuring that prior knowledge is available as they adapt to new information. With short-term memory, agents recall the current context and refine their approach dynamically, adjusting plans as the user provides more information. Long-term memory can store historical data or long-term objectives, allowing agents to track progress, learn from past interactions, and improve their decision-making over time. This adaptability makes agentic systems highly responsive to evolving user needs, giving them the capacity to self-reflect and improve continuously.

How Agentic RAG Systems Work

In agentic RAG systems, the flow of interaction involves a series of coordinated components that work together to deliver intelligent, adaptive results.

Credits: Weaviate.io

The process is dynamic, with multiple agents collaborating in a multi-agentic RAG environment to handle complex queries. Here's how it all comes together:

Planner

The first step is planning. When a query is received, the planner evaluates the user's intent and devises a strategy for how to approach the task. This includes breaking down complex problems into smaller subgoals, deciding what tools or external knowledge sources are needed, and structuring the query into steps that can be followed iteratively. For example, if the task requires multistep reasoning, the planner might decide to retrieve background information first, then process it step by step, and finally combine it into a final answer.

Retriever

Once the plan is in place, the retriever pulls relevant documents or data based on the context of the query. This involves information retrieval from various external knowledge sources, such as databases or web APIs, using an embedding model to understand semantic relationships and relevance. The retriever is critical in ensuring that the system has the most up-to-date and accurate content to work with, often leveraging context retrieval techniques to gather the most pertinent information for the current task.

LLM Generator

After gathering relevant documents, the LLM generator is responsible for generating a response. It takes into account the context provided by the planner and the retrieved documents to produce a coherent and accurate output. The LLM’s role is to synthesize information, apply multistep reasoning, and craft a response that addresses the query effectively, making use of the knowledge retrieved.

Memory Module

The memory module plays a crucial role in maintaining continuity throughout the interaction. It stores intermediate results, facts, and the evolving intent of the user across multiple steps. This semantic caching of information ensures that the system can adapt dynamically to changes in context and refine its approach as the task progresses. Whether it's recalling a fact from a previous step or keeping track of long-term user preferences, the memory module helps the system stay aligned with the user's needs.

Tooling Layer

A tooling layer adds another layer of complexity and flexibility. This is where external tools and actions, like function calling or database lookups, are triggered to perform tasks that are outside the scope of the language model itself. For example, the tooling layer might call an API to retrieve real-time data or perform an action like making a recommendation based on dynamic user inputs. This capability allows the system to perform beyond basic information retrieval and enables automation of more complex workflows.

The Interaction Loop

The core of agentic RAG is a continuous feedback loop:

1. Goal: The agent defines the user’s goal (e.g., solving a problem, answering a question).

2. Plan: The planner decides how to break down the goal into actionable steps.

3. Retrieve: The retriever gathers relevant documents or data.

4. Act: The system uses an LLM to process the data and generate an answer.

5. Update Memory: Results and intent are stored for future reference, allowing the agent to adapt over time.

This loop enables the system to dynamically adjust its behavior based on new information, refine its approach, and interact intelligently with users in a more adaptable way.

Real-World Patterns and Applications

As agentic RAG systems continue to evolve, their applications across industries are becoming more diverse. By combining retrieval-augmented generation with agentic behavior, these systems enable new workflows that go beyond simple queries. Below, we explore three key real-world patterns where agentic AI RAG systems are making a significant impact.

Research Agents

In industries like legal, academic, or market research, agents are increasingly used to automate complex research tasks. These research agents decompose queries into manageable sub-steps, iterating on retrieved data until a complete picture emerges. Instead of fetching a single document and delivering a response, these agents continuously refine the process by retrieving multiple documents, analyzing their relevance, and using multi-agentic RAG workflows to combine the information into a comprehensive result.

For example, in legal research, an agent might break down a query about a specific case law into steps: first retrieving statutes, then relevant case precedents, and finally analyzing them to generate a legal argument. The agentic graph RAG model is especially useful here, where agents can maintain an evolving structure of knowledge through iterative retrieval and reasoning.

Code + API Assistants

Code and API assistants represent a growing use case in software development. These agents facilitate the generation of code, retrieval of documentation, and testing of outputs in a multi-step process. For instance, a developer might ask for assistance in building an API endpoint. The agent will first retrieve the relevant API documentation, then generate code based on that data, followed by testing the output to ensure correctness.

A multi-agentic RAG setup is ideal for this workflow, where each agent handles a specific part of the process: one agent retrieves documentation, another generates the code, and a third tests the output. With the ability to manage such tasks sequentially and efficiently, these agents free up developers to focus on more creative aspects of their work.

Internal Knowledge Assistants

Internal knowledge assistants are becoming increasingly popular within organizations. These agents are responsible for retrieving secure, confidential information from a company’s internal knowledge base. When queries are vague or ambiguous, the agents initiate clarifying steps to narrow down the exact requirements. For instance, a user might ask a broad question about company policy, and the agent will first retrieve general policy documents before asking clarifying questions to refine the search.

These assistants rely heavily on turbo retrievers to quickly gather relevant company documents while maintaining secure access. Moreover, agentic AI RAG capabilities, like memory and planning, allow them to handle complex, multi-step retrieval processes and adapt to shifting needs.

Operational Realities of Agentic RAG

As organizations scale agentic RAG systems, several operational factors must be considered to ensure smooth, efficient performance. While the potential of agentic AI RAG is vast, there are design decisions, architectural choices, and common pitfalls that teams need to navigate. Below, we explore key considerations in scaling these systems and how to avoid common issues.

Scaling Agentic Systems

Scaling agentic systems requires careful decisions about architecture and retrieval strategies. Teams must choose between centralized or decentralized agent architectures, depending on the complexity of the tasks and the level of coordination required among agents.

• Centralized Architectures: A single agent controls the entire process, making decisions for every step. This approach simplifies coordination but may struggle with scalability for more complex queries or large datasets.

• Decentralized Architectures: Multiple agents operate independently but collaborate as needed, enabling more efficient scaling for large, complex workflows. In these systems, each agent may handle specific tasks (e.g., retrieving data, performing computations, or synthesizing results), allowing for greater flexibility in executing multi-step processes.

When it comes to retrieval, teams must also decide between global retrieval, where information is fetched once at the beginning of the process, or per-step retrieval, where data is retrieved as needed at each stage of the workflow. While global retrieval can be more efficient for simpler tasks, per-step retrieval provides flexibility, ensuring that the data is always relevant to the current stage of processing.

However, scaling also introduces challenges in latency management. For planning-heavy workflows, which involve multiple steps of reasoning, the time it takes to complete each step can add up. To mitigate this, retriever mini chunking can be used to break down large datasets into smaller, manageable pieces, ensuring faster retrieval and reducing response times.

Common Pitfalls to Avoid

When designing agentic RAG systems, it’s crucial to anticipate potential issues that could undermine performance or accuracy.

1. Looping Agents: One common pitfall is agents that get "stuck" in loops, unable to converge or overshoot the scope of their task. This typically happens when an agent doesn't have clear boundaries for when to stop or move on to the next step. Proper goal definition and step-by-step planning can help avoid this problem.

2. Ungrounded Generations: Agents that fail to ground their generation in accurate data can lead to hallucinations, plans based on incorrect or imagined facts. Using a retriever mini chunk strategy ensures that each step is supported by relevant, verifiable data, reducing the likelihood of these errors.

3. Evaluation Mismatches: In some cases, individual sub-steps may be executed correctly, but the final output still falls short. This is often due to a mismatch between the sub-agent tasks and the overall goal. It’s important to test the entire agent workflow as a whole, ensuring that the final result aligns with user expectations. For example, using multi-agentic RAG workflows with continuous feedback loops can help improve final outcomes.

4. Cost Blowups: A key consideration is the risk of over-fetching or inefficient planning patterns. If an agent retrieves too much unnecessary data, it can significantly increase costs, especially in systems where queries involve large-scale information retrieval. Using tools like LlamaIndex’s query engine can help optimize retrieval by fetching only the most relevant data at each step, improving efficiency and reducing costs. However, users have reported it having a steep learning curve and some challenges with integration complexity, particularly when trying to align it with existing workflows or larger systems.

Tooling Landscape for Agentic RAG

As agentic RAG systems become more sophisticated, a variety of tools are available to support the development of agentic behaviors, planning, and multi-step workflows. However, each tool comes with its own set of strengths and trade-offs. Below are some of the most widely used tools in the space.

LangChain

Credits: Langchain

LangChain is a powerful framework designed for building agentic RAG workflows by chaining various components like retrievers, generators, and planners. It offers flexibility for building complex, multi-step processes that require significant coordination across different AI components. However, LangChain is known for its steep learning curve and verbose architecture, making it more suitable for developers with strong technical backgrounds and those working on prototyping or research projects.

While it excels at chaining tools together in a modular way, its agentic RAG implementation can be challenging to scale for larger, production-grade systems without significant manual intervention.

LangGraph

Credits: Langgraph

LangGraph, developed by the same team behind LangChain, introduces a more structured and stateful orchestration framework for managing complex agentic workflows. Unlike traditional linear pipelines, LangGraph uses a graph structure that enables dynamic decision-making and real-time adjustments during AI interactions. This is ideal for scenarios where agents need to make adaptive choices across multiple steps, such as handling more complex multi-agent setups.

Although it brings a more intuitive approach to managing agentic RAG, LangGraph is still evolving and requires hands-on tuning to ensure optimal performance, especially in multi-agent environments. As such, it may not be the best fit for teams looking for a ready-to-use, production-ready solution without significant configuration.

LlamaIndex

Credits: LlamaIndex

LlamaIndex serves as a flexible retrieval layer for agentic systems, helping teams optimize information retrieval and dynamically fetch the most relevant data at each step of a multi-step process. While it provides powerful capabilities in terms of retrieving data from vector databases, it does not provide the same level of agent orchestration as tools like LangChain or LangGraph.

For teams that require flexible retrieval-augmented generation (RAG) capabilities, LlamaIndex can be an excellent choice. However, its lack of built-in agent management means that it’s better suited for the retrieval portion of an agentic RAG system rather than comprehensive agent orchestration.

Orq.ai for Agentic RAG

Orq.ai offers a comprehensive end-to-end platform designed to take Agentic RAG systems from prototype to production, helping software teams seamlessly integrate LLMs with private knowledge bases for enhanced AI outputs. With built-in orchestration for agents, retrieval flows, memory management, and action tools, Orq.ai simplifies the process of operationalizing Agentic RAG.

Overview of RAG UI in Orq.ai

Key features include:

• End-to-End Orchestration: Streamlined handling of agents, memory management, and retrieval workflows.

• Native Evaluation and Observability: Evaluate AI agents for performance benchmarking. Track every step, debug, and optimize agent flows.

• Human-in-the-Loop Support: Compliant with regulatory requirements and ideal for scalable deployment.

• Reliability in Production: A turnkey solution for operationalizing Agentic RAG at scale.

For teams looking to implement Agentic RAG without the complexities of building everything from scratch, Orq.ai provides the perfect solution.

Book a demo with our team to explore agentic RAG with Orq.ai.

Agentic RAG: Key Takeaways

Agentic RAG systems combine the foundational capabilities of Retrieval-Augmented Generation with agentic behavior, enabling LLMs to plan, adapt, and reason across multiple steps. These systems enhance flexibility, decision-making, and efficiency in AI workflows, making them ideal for tasks like research, customer support, and internal knowledge management.

Operationalizing these systems at scale presents challenges, but tools like Orq.ai simplify the process, offering seamless orchestration, evaluation, and observability to ensure smooth deployment in production environments.