This article discusses the following topics: what is agentic RAG?, how it works, Agentic RAG architecture, its advantages, and its disadvantages.
What is Agentic RAG?
An AI agent-based RAG implementation is referred to as agentic RAG. In particular, it overcomes the constraints of the non-agentic pipeline by integrating AI agents into the RAG pipeline to coordinate its elements and carry out extra tasks beyond basic information creation and retrieval.
How does Agentic RAG work?
The term “agentic RAG” most frequently refers to the usage of agents in the retrieval component, while agents might be used at other phases of the RAG pipeline.
In particular, the utilization of retrieval agents with access to various retriever tools makes the retrieval component agentic. These tools include:
- Similar to standard RAG pipelines, a vector search engine (also known as a query engine) conducts vector search over a vector index.
- Online search engine calculator
- Any API for programmatic software access, including chat or email apps, among many others.
The RAG agent can then use the following sample retrieval scenarios to reason and take action:
- Choose whether or not to retrieve information.
- Select the appropriate tool to obtain pertinent data.
- Create the actual query, assess the context that was retrieved, and determine if it requires a re-retrieval.
Agentic RAG Architecture
The agent is at the heart of the agentic RAG architecture, as opposed to the sequential naive RAG design. The complexity of agentic RAG designs can vary. A single-agent RAG architecture is essentially a straightforward router. Nevertheless, a multi-agent RAG design might potentially include more than one agent. The two basic RAG architectures are covered in this section.
Router, or single-agent RAG
Agentic RAG is a router in its most basic configuration. This implies that the agent chooses which of the at least two external information sources to use to obtain further context. However, (vector) databases are not the only external knowledge sources. Additional information can also be obtained using tools. For instance, you can utilize an API to get more information from your email accounts or Slack conversations, or you can perform a web search.
Multiple-agent RAG Frameworks
Since there is only one agent capable of thinking, retrieval, and answer production, the single-agent system thus has constraints, as you might expect. As a result, chaining several agents together in a multi-agent RAG application is advantageous.
One master agent, for instance, could be in charge of coordinating the retrieval of information from several specialized retrieval agents. One agent might, for example, obtain data from exclusive internal data sources. Information retrieval from your personal accounts, like chat or email, may be the area of expertise for another agent. Retrieving publicly available information from web searches could be the specialty of another agent.
Advantages of agentic RAG
These systems can now respond more accurately, complete tasks on their own, and work with people more effectively due to the switch from vanilla RAG to agentic RAG.
The enhanced quality of recovered supplementary information is the main advantage of agentic RAG. The retrieval agent can direct queries to specialized knowledge sources by adding agents with tool usage access. Additionally, before the retrieved context is used for additional processing, a layer of validation is made possible by the agent’s reasoning abilities. Agentic RAG pipelines can therefore produce replies that are more reliable and accurate.
Disadvantages of agentic RAG
That being said, every coin has two sides. Incorporating an LLM to do a task is known as using an AI agent for a subtask. Using LLMs in any application has drawbacks, including increased latency and instability. An agent might not finish a task adequately (or at all), depending on the LLM’s reasoning abilities. When an AI agent is unable to finish a task, it is crucial to include appropriate failure modes to assist them in getting back on track.
Conclusion
The idea of agentic RAG, which entails adding agents to the RAG pipeline, was covered in this blog. While there are numerous uses for agents in a RAG pipeline, retrieval agents with tools to generalize retrieval are most frequently employed in agentic RAG.
The distinctions between vanilla RAG pipelines and agentic RAG architectures utilising single-agent and multi-agent systems were covered in this article. Numerous frameworks, like LlamaIndex, LangGraph, and CrewAI, are developing for implementing agentic RAG as a result of the growth and popularity of AI agent systems.