AI Agents: Mastering Agentic RAG - Part 5

Iron Contributor

Mar 31, 2025

Hi everyone, Shivam Goyal here! This blog series, based on Microsoft's AI Agents for Beginners repository, continues with a deep dive into Agentic RAG (Retrieval-Augmented Generation). In previous posts (links at the end!), we've explored the foundations of AI agents. Now, we'll explore how Agentic RAG elevates traditional RAG by empowering LLMs to autonomously plan, retrieve information, and refine their reasoning process. I've even created some code samples demonstrating Agentic RAG with different tools and frameworks, which we'll explore below.

What is Agentic RAG?

Agentic RAG represents a significant evolution in how LLMs interact with external data. Unlike traditional RAG, which follows a linear "retrieve-then-read" approach, Agentic RAG empowers the LLM to act as an agent, autonomously planning its information-seeking process. It involves an iterative loop of LLM calls, tool invocations, result evaluations, and query refinements, creating a dynamic "maker-checker" cycle. This allows the agent to dynamically adapt its approach, improving accuracy and handling complex scenarios far more effectively.

Owning the Reasoning Process

Agentic RAG distinguishes itself by granting the LLM ownership of its reasoning. Instead of relying on pre-defined prompt chains, the agent dynamically determines the best course of action. For example, when tasked with creating a product launch strategy, an agentic model might independently decide to:

Retrieve market trends using Bing Web Grounding.
Analyze competitor data with Azure AI Search.
Correlate internal sales metrics using a SQL database.
Synthesize findings using Azure OpenAI.
Evaluate the strategy and iterate if necessary.

This autonomous decision-making is a key characteristic of Agentic RAG.

Implementation: Iterative Loops, Tool Integration, and Memory

Agentic RAG operates through a continuous loop:

Initial Call: The user provides a prompt, defining the agent's goal.
Tool Invocation: The agent selects and uses a tool (e.g., vector database query, SQL call) to gather information.
Assessment & Refinement: The agent evaluates the retrieved data and refines its query or chooses a different tool if needed.
Repeat Until Satisfied: This loop continues until the agent deems its response satisfactory.
Memory & State: The agent maintains memory and state throughout the process, avoiding redundant actions and making informed decisions.

Handling Failure and Self-Correction

Agentic RAG incorporates self-correction mechanisms:

Iteration and Re-Querying: The agent tries alternative search strategies or refines queries upon encountering irrelevant information.
Diagnostic Tools: Agents can leverage diagnostic tools to debug their reasoning process.
Human Oversight: For critical tasks, the agent can request human intervention if needed.

Boundaries and Practical Use Cases

While powerful, Agentic RAG operates within defined boundaries:

Domain-Specific Autonomy: The agent's autonomy is limited to its designated domain.
Infrastructure-Dependent: Its capabilities depend on the integrated tools and data.
Guardrails and Policies: Ethical guidelines and business policies constrain the agent's actions.

Agentic RAG shines in scenarios requiring:

Correctness: Ideal for tasks where accuracy and verification are crucial (e.g., compliance, legal research).
Complex Data Interactions: Effective for navigating and querying structured databases.
Extended Workflows: Handles evolving information needs in longer-running tasks.

Governance, Transparency, and Trust

Building trust in Agentic RAG requires:

Explainable Reasoning: Providing insights into the agent's decision-making process.
Bias Control: Ensuring balanced data sources and mitigating potential biases.
Human Oversight: Maintaining human review for sensitive or critical tasks.