RAG vs Agentic RAG - Clearly Explained

In today’s newsletter:

• Suna AI - Open Source Generalist AI agent

• RAG vs Agentic RAG Clearly Explained

Reading time: 3 minutes.

Suna AI - 100% Open-Source Generalist AI agent

Suna is a fully open-source agent that takes natural-language instructions and turns them into real, autonomous actions.

It combines tools like web scraping, file editing, API calling, and sandboxed execution to chain multi-step workflows.

Key Features:

• Browser Automation: Auto-navigate sites, fill forms, and scrape data.

• File Handling: Securely create and manage files in a sandboxed environment.

• Web Crawling & Search: Gather real-time info across the web.

• Shell Execution: Run scripts safely via Docker containers.

• API Integration: Connect external services for seamless automation.

It’s 100% Open Source

👉 Check out Github Repo

RAG vs. Agentic RAG: Clearly Explained

Large Language Models (LLMs) generate responses based only on their training data, which can lead to gaps when answering questions about recent events or specific domains.

Retrieval-Augmented Generation (RAG) improves this by allowing models to retrieve relevant information from external sources at query time. This helps ground the response in accurate, current, and context-specific content.

But for more complex tasks, basic RAG has its limits. That’s where Agentic RAG comes in. It gives the ability to reason, adapt, and handle multi-step problems, making it a better fit for real-world challenges.

Visual Comparison: How RAG and Agentic RAG Work ?

RAG Flow:

• Query: The user submits a query.

• Retrieval: The query is converted into a vector using an embedding model. This vector is used to search a vector database for relevant documents or context.

• Augmentation: The retrieved information is combined with the original query to form an augmented prompt.

• Generation: The augmented prompt is passed to a LLM, which generates a context-aware response.

Key Limitation:

This workflow is static. It retrieves information from a vector database during the initial step. If the model needs information beyond what is stored in the database, such as live web data or access to external tools, it cannot obtain it within this setup.

That’s where Agentic RAG steps in, giving the system the ability to reason, reflect, and iterate across multiple steps.

Agentic RAG Flow :

• Query: The user submits a query.

• Agentic Reasoning: An LLM agent evaluates if enough information is available.

  • If yes, the context and query are sent to the LLM for generation.

  • If no, the agent autonomously decides which sources to use (vector database, web search, tools/APIs, MCP), then retrieves more information, and repeats the process.

• LLM generates a response with the new context.

• Iteration: The agent can rewrite queries, seek clarifications, and validate answers, iterating until a satisfactory result is produced.

Note: The Agentic RAG flow is not fixed. It’s a flexible approach that can change based on the task. Depending on the setup, agents can plan, retrieve, ask questions, and reason in different ways. The main idea is to move beyond one-time retrieval and let AI reflect, adapt, and improve its answers step by step.

Key Strength: Agentic RAG introduces flexible, multi-step reasoning and dynamic retrieval. The agent can plan, adapt, and orchestrate tool use, making the system robust for complex, real-world tasks.

Why Traditional RAG Falls Short

• Single-pass Retrieval: If the first retrieval doesn’t yield enough context, the LLM cannot fetch more or clarify the query.

• No Multi-step Reasoning: Complex queries requiring information from multiple sources or iterative refinement are poorly handled.

• Lack of Adaptability: The system cannot change strategy or interactively engage with the user or external tools.

How Agentic RAG Solves These Issues

• Agentic Planning: Agents can rewrite queries for clarity, decide what data is needed, and select the best retrieval sources.

• Multi-source Retrieval: Beyond vector databases, agents can tap into web search, APIs, and other tools.

• Iterative Validation: Agents check if the generated answer is relevant and accurate, iterating as needed.

• Autonomous Action: The system adapts its approach dynamically, making it ideal for tasks that require reasoning, summarization, comparison, or follow-up questions

How to Choose the Right Approach

• RAG is cost-effective, keeps LLMs up-to-date, and avoids expensive fine-tuning.

• Agentic RAG enables robust, accurate, and adaptable AI critical for enterprise use, customer support, research, and any scenario demanding reliable, multi-step reasoning

Traditional RAG grounds LLMs in real data and works well for most use cases. It follows a single-pass, non-adaptive flow. Agentic RAG adds reasoning, tool use, and iteration, making it better suited for complex tasks.

For most use cases, RAG is enough. If your application needs deeper interaction or dynamic decision-making, Agentic RAG is worth exploring. Choose based on your use case.

That’s a Wrap

That’s all for today. Thank you for reading today’s edition. See you in the next issue with more AI Engineering insights.

PS: We curate this AI Engineering content for free, and your support means everything. If you find value in what you read, consider sharing it with a friend or two.

Your feedback is valuable: If there’s a topic you’re stuck on or curious about, reply to this email. We’re building this for you, and your feedback helps shape what we send.

WORK WITH US

Looking to promote your company, product, or service to 120K+ AI developers? Get in touch today by replying to this email.