Ever feel like your AI knows how to fetch answers but not how to think? That’s the problem with traditional RAG. It’s like having an intern who’s great at finding documents but clueless about what to do with them.
Enter agentic RAG — the AI that doesn’t just retrieve information but actually connects the dots. It reasons, plans, and synthesizes. It’s the difference between getting a stack of reports and getting a clear, actionable strategy.
When your users ask complex, layered questions, which one would you rather have?
You probably already have some ideas about when you could and should use agentic RAG—but if not, you will in about 5 minutes, by the end of this post.
Traditional RAG (Retrieval-Augmented Generation) works by finding relevant information in a database and using it to enhance AI responses. It follows a simple, linear process:
Take user input 🠂 Search for related content in a vector database 🠂 Add found information to the prompt 🠂 Generate an answer.
This approach works well for straightforward questions that match what's in your knowledge base.
Agentic RAG, however, adds a layer of intelligence to this process. Instead of just matching and retrieving, it:
Think of traditional RAG as a library assistant who can only look up one book at a time. Agentic RAG is like having a research team that can check different resources, connect ideas, and produce a cohesive report.
Traditional RAG systems hit limitations quickly when faced with complex, real-world questions. Here's why agentic RAG has become necessary:
Complex queries need multi-step thinking
When users ask "Compare the sales performance of our East and West regions last quarter," a simple vector search won't cut it. This requires:
Different query types need different search methods
Traditional RAG relies primarily on semantic similarity, which fails with questions like:
Users ask conversational follow-ups
In a conversation flow, context matters. When a user asks "What about last year's numbers?" after discussing this year's budget, traditional RAG struggles to maintain this thread.
Resource efficiency
Running expensive embeddings and vector searches for every question wastes resources, especially when simpler SQL queries would work better for certain questions.
This isn't a simple yes/no question. Each approach has its place:
When normal RAG shines:
When agentic RAG is better:
Let’s also take a look at what a vector store search is in the context of agentic RAG and how it’s different from a normal SQL search.
A vector store holds so called ‘embeddings’, which are numerical representations of text (or other data) in a high-dimensional space. These embeddings capture the meaning of the content—not just the exact words. When you search, your query is also turned into an embedding. The vector store finds results by measuring how similar (often using cosine similarity) your query’s vector is to the vectors in the store. This is known as semantic search.
SQL databases store structured data (like tables with columns and rows). SQL search retrieves records based on specific, rule-based queries—often matching exact words, values, or patterns. Your query could look something like: SELECT * FROM users WHERE email = 'example@email.com'.
So a basic SQL search is perfect when you know exactly what you’re looking for while a vector search is better for finding content that’s contextually related, even if it doesn’t use the exact words of your query. It works well with long, unstructured text like articles, support docs, or transcripts and can handle fuzzy, natural language.
Why this matters in RAG:
Vector store is ideal when your AI agent needs to retrieve knowledge from large, unstructured datasets (like articles or support content). It enables more flexible, intuitive responses based on meaning.
SQL search works better for pulling factual, structured data — like pulling a learner’s progress from a course database or getting course details based on exact filters.
The best RAG systems use both. In addition, Mindset AI also utilizes knowledge graphs to deliver the best results.
A knowledge graph captures relationships and context between different pieces of knowledge. It is a network of entities (like people, concepts, products) and their relationships (like “teaches,” “belongs to,” “is part of”)—more like a web of meaning than a list of records. For example:
Learner X completed Course A
This structure makes it easy to answer questions like:
In agentic RAG, a knowledge graph can be a powerful retrieval tool because it organizes knowledge with context and connections. All these tools together make Mindset AI agents smarter and more context-aware.
So is regular or agentic RAG better, then? In many advanced applications, the answer is increasingly becoming agentic RAG, as it provides flexibility to handle both simple and complex queries within a single system.
Agentic RAG offers specific advantages that address common challenges in EdTech. Here's when you should consider implementing it:
As an EdTech product leader, you know personalized learning is essential. Agentic RAG enables:
Modern L&D requires flexibility in content delivery: some people learn best through video demonstrations, others prefer reading comprehensive text explanations, and some need interactive exercises and simulations. And most benefit from a mix of all these approaches
Agentic RAG can understand different content formats and learn which ones are most effective in what scenarios. As a result, it can recommend videos, or audio, or text to meet specific learning objectives and student preferences.
Effective learning requires timely, relevant feedback such as automatically evaluating learner responses against expected answers, providing explanations tailored to specific mistakes, generating practice questions based on identified weak areas, and tracking progress across multiple learning objectives
Agentic RAG can manage the entire feedback loop, not just retrieve information about correct answers.
For workplace learning and professional development, connecting theoretical concepts with practical examples from your content library is essential.
Finding case studies relevant to a learner's specific industry or role, suggesting applications of knowledge that match a learner's stated goals, and building connections between static content and workplace skills—all capabilities that make for a great experience. And agents help achieve all this.
For online learning platforms serving diverse audiences, translating the language of the materials is not good enough. You must localize the content:
In a nutshell: Implement agentic RAG when you need to move beyond simply retrieving information to creating intelligent, adaptive learning experiences that can scale across subjects, adapt to individuals, and support the full learning cycle.
Mindset AI's multi-agent system tackles a fundamental limitation of traditional RAG: the inability to effectively search for specific files or resources (we call this the "Find" capability).
The multi-agent solution
At the heart of our approach is a director agent (the "Librarian Agent") that orchestrates the retrieval process:
You as a user talk to the main agent. The main agent calls the RAG director agent, and this then has a panel of experts it asks. Our AI & ML expert, Wic Verhoef, recently talked about this topic in-depth in an interview about the benefits and drawbacks of agentic systems.
The system excels with complex comparative questions. For example, if asked to "compare chalk and cheese," it would:
Find information about chalk 🠂 Find information about cheese 🠂 Synthesize a comparative answer
This allows for answering questions that span different topics, even when no single document contains the complete answer.
Key benefits for learning environments
Mindset AI's approach demonstrates how agentic RAG can create more intelligent, adaptable systems that bridge the gap between simple information retrieval and the complex needs of educational environments.
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