.jpg)
In the latest episode of the RAG Masters show, we explore Agentic RAG, different techniques to build, integrate, and evaluate it, real-world use cases, and future challenges the field might face.
Understanding Agentic RAG: A Technical Breakdown
To leverage Agentic RAG effectively, let's first break down its core components and how they integrate.
At its core, RAG enhances language models with external knowledge. RAG fundamentally helps to augment prompting by retrieving information from a store of documents or data and then passing key pieces of information to the language model.
AI Agents, meanwhile, are systems designed to reason, act, and observe in a continuous loop. They make decisions, use tools, and adapt to new information - mimicking human problem-solving processes.
Agentic RAG combines these approaches. It creates a system that retrieves and uses information from both documents and the environment, and it can make decisions about how to use that information in a broader context. It's akin to developing a super smart assistant with a vast knowledge base and the ability to apply that knowledge to solve complex problems.
The ReAct Architecture: Implementing the Core of Agentic Systems
The ReAct architecture (not to be confused with the JavaScript library of the same name) forms the backbone of many Agentic RAG systems. ReAct, in this context, stands for Reasoning and Act.
RAG Masters co-host Daniel Warfield describes it as "a pretty rigid structure where you ask a question, then you tell the model a few things. For example, I want you to break it into sections and every section I want you to think of something specific."
Watch the full clip for a more detailed breakdown of ReAct:
This architecture creates a cycle of thought, action, and observation. The agent thinks about what it needs to do, takes an action (which could be using a tool like RAG or making a specific API call), observes the result, and then thinks again. This cycle allows the agent to break down complex tasks and approach them systematically.
Flexibility and Multiple Tools: Beyond Simple RAG
A key advantage of Agentic RAG is its flexibility. While RAG on its own is a powerful tool, it's not the only one an agent can use. In fact, an agent could access a whole toolkit of different functions depending on its purpose and goal.
Warfield points out, "You can have the agent request a type of tool they might want and then get back that tool, which can be done with RAG. So not only are the tools themselves RAG, you can build a retrieval engine for retrieving tools that are described textually."
This approach allows the agent to become a flexible problem-solver, adapting its strategy based on the task at hand.
Real-World Applications: From Medical Claims to Call Centers
The real-world applications for Agentic RAG are varied and have not yet been fully explored.
In the medical field, for example, Agentic RAG could revolutionize claim processing. Instead of a simple keyword search, a system with this tech under the hood could understand the context of a particular claim, cross-reference it with medical knowledge from a structured database, and then make nuanced decisions about its validity and take action based on its decision.
In customer service, the impact is already being felt. As noted in the podcast, "For example, call centers. There's been some call center applications that are scary good at traversing the standard call center script where you have a graph… basically they build an agent that kind of goes through and has text to speech and speech to text on top." Some of these early systems are already highly effective and can understand customer queries, retrieve relevant information, and navigate complex decision trees to provide accurate and helpful responses.
As the technology advances and techniques in both Agentic AI and RAG improve, it’s likely we’ll see more and more complex approaches spread through different industries over time.
Performance Metrics and Evaluation
When implementing Agentic RAG systems, it's crucial to put evaluation metrics in place and accurately track them. These are a few example key indicators an evaluation might include:
1. Task Completion Rate: The percentage of tasks the agent successfully completes based on a specific rubric or success scale.
2. Decision Accuracy: How often the agent makes the correct decision or provides accurate information.
3. Response Time: The time taken to complete a task or provide a response.
4. Tool Usage Efficiency: How effectively the agent uses its available tools.
Challenges and Considerations: The Hallucination Problem
Agentic RAG has its potential challenges and pitfalls, as with any sophisticated AI system. One of the most significant issues is hallucination - when AI systems generate plausible-sounding but incorrect information.
This problem gets amplified in Agentic systems due to their complexity and the number of potential variables that could go awry. If one part of the system starts to hallucinate, it may cause the agents to experience a sort of shared hallucination that poses risks for reliability as the clip below describes.
When it comes to verifying the outputs and functionality of an Agentic RAG system, there are a number of challenges to consider. Verifying a system at each step of the process can quickly become unwieldy as the system grows in complexity.
As Warfield notes in the below clip, "The verification process of an agentic system is the same as the verification of RAG, but way harder because now it's also wrapped around an agent. So you can still have the core RAG that fails, and then you can also have the agent that fails, and it can fail in terms of how it thinks, in how it structures the tool execution...it can snowball really quickly."
While there is no silver bullet for a perfect Agentic system, the following strategies could help to mitigate hallucinations:
1. Fact-checking: Cross-reference generated information with trusted sources.
2. Confidence scoring: Implement a system where the agent rates its confidence in its outputs.
3. Human-in-the-loop validation: For critical applications, include human oversight to verify important decisions.
Integration with Existing Systems
Integrating Agentic RAG into existing systems has a lot of potential, but requires careful architectural planning.
Here's one high-level approach:
1. Define clear APIs for communication between the Agentic RAG system and existing components.
2. Build a robust data pipeline to feed relevant information into the RAG knowledge base.
3. Design a feedback mechanism to continuously improve the agent's performance based on real-world interactions.
4. Implement proper error handling and fallback mechanisms for when the agent fails or produces low-confidence results.
Conclusion
As we look to the future of Agentic RAG, it's clear that it’s a powerful but complex technology.
The episode closes with an apt analogy: "What do we do with this crazy fast car we just got? Maybe in the next episode Daniel tries to figure out how to drive AI without crashing the car."
This describes the current state of Agentic RAG - we have a powerful vehicle, but we're still learning how to drive it safely. The technology may be production-ready in some areas, especially where the problem space is well-defined like for some call center applications. However, for more open-ended or critical applications, careful design and testing are crucial.
As developers, our challenge is to harness the power of Agentic RAG while managing its complexities. This involves not just understanding the technical aspects of implementation and integration, but also tackling issues of reliability, user experience, and more.
The potential for Agentic RAG is huge. It could be the backbone for AI systems that are more flexible, more capable, and better able to handle complex, multi-step tasks. But realizing this potential in production-ready applications will require ongoing research, careful implementation and testing, and a deep understanding of both the capabilities and limitations of these systems.
You can watch the full Agentic RAG episode of RAG Masters:
