Table of Contents
What is Agentic RAG?
Agentic RAG is a method that uses AI agents to control how a language model retrieves information and generates responses. Unlike traditional systems that follow a fixed process, the agent decides which action to take next, such as running a search or accessing a company database.
In financial services, for instance, an agent might identify the reason behind a payment issue by consulting transaction records and then verifying the relevant policy before responding.
It builds on retrieval-augmented generation, where models incorporate external data to improve the quality of their answers. Agentic RAG goes further by allowing the system to plan its steps and adjust based on the results it receives.
To support this behavior, the model is connected to systems that let the agent trigger searches or call business tools. This enables a more flexible process that can move through multiple stages, making it more effective for complex customer queries or internal research tasks.
How does agentic RAG work?
Deloitte reports that 50% of companies using generative AI will explore agentic systems through pilot projects or proofs of concept by 2027.
Agentic RAG supports this direction by enabling AI agents to manage tasks that unfold across multiple steps, using planning and tool selection as part of an agentic AI workflow to reach a complete response.
Understanding how the process works is essential for teams preparing to apply agent-based models in real workflows, so here is an overview:
Initializing the agent workflow
An agent is launched when the system receives a query. From the start, it prepares to manage the task in stages. It keeps track of its current position in the process and builds a working memory of helpful information. For example, in a customer support setting, the agent typically begins by identifying the case ID before determining which systems to access next.
Planning and breaking down the query
The agent reviews the input to understand what outcome the user expects. If the question concerns a declined claim, the agent may need to understand the reason behind the decision before determining the next steps.
Retrieving information using external tools
Once the plan is in place, the agent selects a tool that fits the next step. It may query a claims database, trigger a search function, or look up internal policy details, depending on the query’s requirements.
Validating and refining the retrieved data
The agent checks whether the information it receives is relevant and complete. If the answer lacks context or doesn’t match the original goal, the agent can adjust its plan. It might issue a follow-up query or switch to a different data source.
Generating a response with integrated context
With the necessary information gathered, the agent passes it to the large language model. The model uses what the agent has assembled to generate a response that reflects the full context of the task. In the case of a declined claim, the output could explain the reason and suggest a follow-up action, grounded in retrieved records.
Agentic RAG vs. Traditional RAG
Agentic RAG differs from traditional RAG in its approach to problem-solving.
- Traditional RAG retrieves information and generates a response in a single step.
- Agentic RAG allows an AI agent to control a multi-stage process.
Here is an overview of the key differences between the two approaches:
| Agentic RAG | Traditional RAG | |
| Data access | Accesses data across multiple steps, often from varied sources | Retrieves documents in one step using a fixed query |
| Task approach | Interprets the goal, breaks it into parts, and plans how to solve it | Responds directly to the user’s input without breaking it down |
| Tool use | Chooses and triggers different tools based on the task’s evolving needs | Uses a single retriever to fetch documents |
| Adaptability | Adjusts its actions if earlier steps produce incomplete or unclear results | Follows a fixed path and cannot revise its process |
| Self-improvement | Maintains context throughout the process and can reflect on prior steps | Lacks memory or reasoning to build on past tasks |
These differences make agentic RAG better suited for tasks such as investigating insurance claims or navigating complex regulatory inquiries.
Agentic RAG use cases
As AI adoption grows, agentic RAG is emerging as a way to handle queries that span multiple systems or require flexible reasoning.
Below are three ways this approach supports decision-making in enterprise environments.
Adaptive clinical decision support
Clinicians often require assistance in interpreting symptoms within the context of a patient’s history and current guidelines. In a case involving conflicting test results, the system may begin by retrieving the patient’s recent diagnostic reports, then consult clinical guidelines to compare potential treatment paths. If those sources don’t offer enough clarity, the agent can look for related cases or request follow-up input from the user before offering a recommendation.
Context-aware product discovery
Retail environments often receive vague product queries that require interpretation beyond traditional enterprise search capabilities. When a shopper asks for a reliable laptop for travel, the agent may start by filtering models with extended battery life. If availability is limited, it can reframe the request and search across nearby stores or alternative models without pausing the task. The system remains focused on refining options until the most relevant product is found.
Proactive financial risk monitoring
Compliance teams in finance need to investigate activity that may signal risk. A query about unusual withdrawals might prompt the system to scan related accounts and locate internal risk guidance. As new details emerge, the agent shifts focus toward gathering supporting evidence from regulatory updates or past case reviews, continuing until it can produce a clear report.
FAQs
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Agentic RAG generally uses more computational power. The agent navigates multiple steps and often retains context as it works. It may also trigger external tools during a task, which increases the system load compared to a single retrieval-and-response approach.
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Agentic RAG can be added to existing systems by layering agent functions on top of the current retrieval process. Many setups support this through established AI agent frameworks. Many setups allow this without changing the base model. The success of integration depends on how flexible the original system is and what types of tasks it needs to support.
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Privacy controls depend on how the agent is deployed. It can be limited to secure environments and designed to access only approved data. Additional measures, such as access control or activity tracking, help ensure the agent works within defined security boundaries.