RAG vs. Agentic RAG: A Deep Dive with a CrewAI Implementation Example

Introduction

Contents

Part 1: Understanding RAG and Agentic RAG Part 2: Key Differences Between RAG and Agentic RAG Part 3: The Limitations of Traditional RAG Part 4: Key Components of an Agentic RAG System 4.1 Routing Agents 4.2 Query Planning Agents 4.3 Tool Use Agents 4.4 ReAct Agents 4.5 Dynamic Planning and Execution Agents Part 5: Building an Agentic RAG System with CrewAI 5.1 Define Agents 5.2 Define Tasks 5.3 Crew.py File 5.5 The Output Part 6: Challenges in Deploying Agentic RAG The Future of Agentic RAG Further Reading and Resources

Retrieval-Augmented Generation (RAG) has revolutionized how large language models (LLMs) interact with external knowledge, but as AI demands grow more complex, Agentic RAG has emerged as a transformative evolution. This blog explores the differences between RAG and Agentic RAG, their architectures, and practical implementation using CrewAI, a framework for orchestrating collaborative AI agents. By the end, you’ll understand how to build an Agentic RAG system that dynamically routes queries, retrieves context, and generates precise answers.

By the end of this post, you’ll have a comprehensive understanding of Agentic RAG and its transformative potential in AI-driven systems.

Part 1: Understanding RAG and Agentic RAG

What is RAG?

Traditional RAG combines retrieval from external knowledge bases (e.g., vector databases) with LLM-based generation. Its workflow involves:

Retrieval: Fetching relevant documents using semantic search.
Augmentation: Injecting retrieved data into the LLM’s prompt.
Generation: Producing a response grounded in the retrieved context.

Limitations of RAG:

Static retrieval: No iterative refinement of queries.
Limited adaptability: Cannot use external tools (e.g., web search, calculators).
No verification: Retrieved data is used “as-is” without cross-checking.

What is Agentic RAG?

Agentic RAG introduces autonomous AI agents to overcome RAG’s limitations. These agents:

Analyze and decompose queries into sub-tasks.
Use tools (web search, APIs, calculators) to gather real-time data.
Verify and refine responses iteratively.

Key Advantages:

Dynamic query optimization: Agents rephrase ambiguous queries for better retrieval.
Multi-step reasoning: Break down complex tasks (e.g., comparing financial reports).
Self-learning: Adapt based on user feedback.

Agentic RAG represents an evolution of the RAG framework by integrating intelligent agents into the retrieval and generation process. Instead of a static pipeline, Agentic RAG introduces a layer of autonomy and dynamic decision-making. Here’s what differentiates it:

Autonomous Agents: Specialized software agents can assess the query, decide which data sources to tap, and even decompose complex queries into smaller, manageable tasks.
Dynamic Query Decomposition: For multifaceted queries, agents break the problem into sub-queries, execute them in parallel or sequentially, and then synthesize the results into a final coherent answer.
Iterative Reasoning: By iterating through retrieval and generation cycles, agents can refine their results—ensuring that the final output is both accurate and contextually rich.
Tool Integration: Agentic systems can interface with external tools (APIs, databases, custom functions) to gather additional data or perform specialized tasks, greatly expanding their capabilities.

This enhanced approach allows Agentic RAG systems to handle more complex, dynamic queries that require not just retrieval and generation, but also planning, reasoning, and adaptive decision-making.

Part 2: Key Differences Between RAG and Agentic RAG

Feature	Traditional RAG	Agentic RAG
Query Handling	Single-step retrieval and generation	Multi-step reasoning with dynamic task decomposition
Decision Making	Relies on static prompt engineering	Uses agents to autonomously decide which tool or data source to use
Adaptability	Limited to pre-defined retrieval methods	Adapts in real time using routing, query planning, and tool integration
Complex Query Support	Best for straightforward Q&A	Excels at complex queries, including context-aware follow-ups
Transparency & Validation	Often lacks detailed source validation	Provides transparent, verifiable citations by dynamically selecting sources

Agentic RAG’s modularity and ability to integrate multiple tools empower it to handle nuanced tasks such as generating follow-up questions, cross-referencing diverse data, and dynamically adapting the retrieval strategy—all crucial for sophisticated applications.

Part 3: The Limitations of Traditional RAG

While traditional RAG is a significant step forward, it comes with several challenges:

Static Retrieval Processes: Traditional systems rely on a fixed retrieval strategy that may not adapt well to complex or ambiguous queries. They often lack the ability to iterate or refine the query based on intermediate results.
Limited Multi-Step Reasoning: Without the capacity to break down a query into smaller sub-tasks, these systems can struggle with multi-faceted questions that require sequential reasoning.
No Autonomous Decision-Making: The process is generally linear, with no mechanism to decide dynamically which external tools or additional data sources might improve the final answer.
Inefficient Handling of Complex Tasks: When tasks involve integrating data from multiple sources or require real-time updates, traditional RAG systems may generate superficial or incomplete answers.

These limitations set the stage for a more advanced system—one that not only retrieves and generates but also thinks, plans, and acts. This is where Agentic RAG steps in.

RAG vs. Agentic RAG: A Deep Dive with a CrewAI Implementation Example 3

Part 4: Key Components of an Agentic RAG System

To better understand Agentic RAG, let’s break down its core components and the roles they play:

4.1 Routing Agents

Routing agents serve as the first point of contact. They analyze the incoming query and decide which retrieval methods or data sources are most appropriate. For example, if a query involves code generation, the routing agent might direct the request to a specialized database of code snippets. Their primary function is to streamline the process and ensure that the right data is fetched for the given context.

4.2 Query Planning Agents

Complex queries often contain multiple facets that require separate handling. Query planning agents decompose these queries into sub-queries. Each sub-query is then processed individually, and the results are later integrated into a cohesive final answer. This modular approach enhances the system’s ability to handle nuanced and multi-part questions.

4.3 Tool Use Agents

Sometimes, retrieving documents alone is not enough. Tool use agents come into play by invoking external functions or APIs. For instance, if a query requires performing a mathematical calculation or fetching live data from an external API, the tool use agent will handle these additional actions. They effectively extend the system’s capabilities beyond textual data retrieval.

4.4 ReAct Agents

ReAct (Reasoning and Acting) agents integrate iterative reasoning with action. They continuously refine the query based on feedback, perform necessary actions, and evaluate intermediate outputs. This iterative process allows the system to correct its course if the initial retrieval is insufficient or if new insights emerge during the process.

4.5 Dynamic Planning and Execution Agents

For even more complex scenarios, dynamic planning and execution agents create a roadmap or computational graph of the tasks that need to be performed. They decide the order of operations, manage dependencies, and ensure that each step is executed optimally. This high-level planning is essential for tasks that require a sequence of actions and cannot be solved in a single pass.

Together, these components transform a traditional RAG system into a dynamic, intelligent framework capable of handling a broad spectrum of real-world queries.

Part 5: Building an Agentic RAG System with CrewAI

CrewAI simplifies creating multi-agent systems where specialized agents collaborate. Let’s build a system that routes queries to a vector store (for domain-specific questions) or the web (for real-time topics).

5.1 Define Agents

router_Agent:
  role: >
    Router
  goal: >
    Route user question to a vectorstore or web search
  backstory: >
    You are an expert at routing a user question to a vectorstore or web search .
    Use the vectorstore for questions on transformer or differential transformer.
    use web-search for question on latest news or recent topics.
    use generation for generic questions otherwise
  llm: azure/gpt-4o

retriever_Agent:
  role: >
    Retriever
  goal: >
    Use the information retrieved from the vectorstore to answer the question
  backstory: >
    You are an assistant for question-answering tasks.
    Use the information present in the retrieved context to answer the question.
    You have to provide a clear concise answer.
  llm: azure/gpt-4o

5.2 Define Tasks

router_task:
  description: >
    Analyse the keywords in the question {question}"
    Based on the keywords decide whether it is eligible for a vectorstore search or a web search or generation.
    Return a single word 'vectorstore' if it is eligible for vectorstore search.
    Return a single word 'websearch' if it is eligible for web search.
    Return a single word 'generate' if it is eligible for generation.
    Do not provide any other premable or explaination.
  expected_output: >
    Give a  choice 'websearch' or 'vectorstore' or 'generate' based on the question"
    Do not provide any other premable or explaination.
  agent: router_Agent

retriever_task :
  description: >
    Based on the response from the router task extract information for the question {question} with the help of the respective tool.
    Use the web_serach_tool to retrieve information from the web in case the router task output is 'websearch'.
    Use the rag_tool to retrieve information from the vectorstore in case the router task output is 'vectorstore'.
    otherwise generate the output basedob your own knowledge in case the router task output is 'generate
  expected_output: >
    You should analyse the output of the 'router_task'
    If the response is 'websearch' then use the web_search_tool to retrieve information from the web.
    If the response is 'vectorstore' then use the rag_tool to retrieve information from the vectorstore.
    If the response is 'generate' then use then use generation_tool .
    otherwise say i dont know if you dont know the answer
    Return a claer and consise text as response.
  agent: retriever_Agent

5.3 Crew.py File

from crewai import Agent, Crew, Process, Task, LLM
from crewai.project import CrewBase, agent, crew, task
from crewai_tools import PDFSearchTool
from agenticrag.tools.custom_tool import GenerationTool,SearchTool
import os
from dotenv import load_dotenv

load_dotenv()

# If you want to run a snippet of code before or after the crew starts, 
# you can use the @before_kickoff and @after_kickoff decorators
# https://docs.crewai.com/concepts/crews#example-crew-class-with-decorators


config = dict(
    llm=dict(
        provider="azure_openai",
        config=dict(
            model="gpt-4o"
        ),
    ),
    embedder=dict(
        provider="azure_openai",
        config=dict(
            model="text-embedding-3-small"
        ),
    ),
)

pdf_search_tool = PDFSearchTool(config=config,pdf='my.pdf')



generation_tool=GenerationTool()
web_search_tool = SearchTool()

@CrewBase
class Agenticrag():
	"""Agenticrag crew"""

	# Learn more about YAML configuration files here:
	# Agents: https://docs.crewai.com/concepts/agents#yaml-configuration-recommended
	# Tasks: https://docs.crewai.com/concepts/tasks#yaml-configuration-recommended
	agents_config = 'config/agents.yaml'
	tasks_config = 'config/tasks.yaml'

	# If you would like to add tools to your agents, you can learn more about it here:
	# https://docs.crewai.com/concepts/agents#agent-tools
	@agent
	def router_Agent(self) -> Agent:
		return Agent(
			config=self.agents_config['router_Agent'],
			verbose=True
		)

	@agent
	def retriever_Agent(self) -> Agent:
		return Agent(
			config=self.agents_config['retriever_Agent'],
			verbose=True
		)

	# To learn more about structured task outputs, 
	# task dependencies, and task callbacks, check out the documentation:
	# https://docs.crewai.com/concepts/tasks#overview-of-a-task
	@task
	def router_task(self) -> Task:
		return Task(
			config=self.tasks_config['router_task'],
		)

	@task
	def retriever_task (self) -> Task:
		return Task(
			config=self.tasks_config['retriever_task'],
			output_file='report.md',
			tools=[generation_tool,web_search_tool,pdf_search_tool]
		)

	@crew
	def crew(self) -> Crew:
		"""Creates the Agenticrag crew"""
		# To learn how to add knowledge sources to your crew, check out the documentation:
		# https://docs.crewai.com/concepts/knowledge#what-is-knowledge

		return Crew(
			agents=self.agents, # Automatically created by the @agent decorator
			tasks=self.tasks, # Automatically created by the @task decorator
			process=Process.sequential,
			verbose=True,
			# process=Process.hierarchical, # In case you wanna use that instead https://docs.crewai.com/how-to/Hierarchical/
		)

5.5 The Output

# Agent: Router
## Task: Analyse the keywords in the question What is AI?" Based on the keywords decide whether it is eligible for a vectorstore search or a web search or generation. Return a single word 'vectorstore' if it is eligible for vectorstore search. Return a single word 'websearch' if it is eligible for web search. Return a single word 'generate' if it is eligible for generation. Do not provide any other premable or explaination.



# Agent: Router
## Final Answer:
generate
```


# Agent: Retriever
## Task: Based on the response from the router task extract information for the question What is AI? with the help of the respective tool. Use the web_serach_tool to retrieve information from the web in case the router task output is 'websearch'. Use the rag_tool to retrieve information from the vectorstore in case the router task output is 'vectorstore'. otherwise generate the output basedob your own knowledge in case the router task output is 'generate



# Agent: Retriever
## Thought: Thought: Based on the router task output, which is "generate", I will use the generation_tool to answer the question "What is AI?"
## Using tool: Generation_tool
## Tool Input:
"{\"query\": \"What is AI?\"}"
## Tool Output:
content='AI, or **Artificial Intelligence**, refers to the simulation of human intelligence in machines that are programmed to think, learn, and make decisions like humans. These systems are designed to perform tasks that typically require human intelligence, such as problem-solving, understanding natural language, recognizing patterns, and adapting to new information.\n\n### Key Components of AI:\n1. **Machine Learning (ML):**\n   - A subset of AI that enables machines to learn from data and improve their performance over time without being explicitly programmed.\n   - Example: A recommendation system on Netflix or Amazon.\n\n2. **Natural Language Processing (NLP):**\n   - The ability of machines to understand, interpret, and respond to human language.\n   - Example: Virtual assistants like Siri, Alexa, or chatbots.\n\n3. **Computer Vision:**\n   - The ability of machines to interpret and analyze visual data, such as images or videos.\n   - Example: Facial recognition or object detection.\n\n4. **Robotics:**\n   - The use of AI to control robots that can perform tasks autonomously or semi-autonomously.\n   - Example: Self-driving cars or robotic arms in manufacturing.\n\n5. **Deep Learning:**\n   - A more advanced subset of machine learning that uses neural networks to mimic the way the human brain processes information.\n   - Example: Image recognition or voice synthesis.\n\n### Types of AI:\n1. **Narrow AI (Weak AI):**\n   - AI systems designed to perform a specific task or a narrow range of tasks.\n   - Example: Spam filters, chess-playing programs.\n\n2. **General AI (Strong AI):**\n   - Hypothetical AI that can perform any intellectual task a human can do, with the ability to reason, learn, and adapt across a wide range of activities.\n   - Example: This level of AI does not yet exist.\n\n3. **Superintelligent AI:**\n   - A theoretical AI that surpasses human intelligence in all aspects, including creativity, problem-solving, and decision-making.\n   - Example: A concept often explored in science fiction.\n\n### Applications of AI:\n- **Healthcare:** Diagnosing diseases, drug discovery, and personalized medicine.\n- **Finance:** Fraud detection, algorithmic trading, and credit scoring.\n- **Transportation:** Autonomous vehicles and traffic management.\n- **Entertainment:** Content recommendations and video game AI.\n- **Customer Service:** Chatbots and virtual assistants.\n- **Manufacturing:** Predictive maintenance and quality control.\n\n### Benefits of AI:\n- Increased efficiency and productivity.\n- Automation of repetitive tasks.\n- Enhanced decision-making through data analysis.\n- Improved accuracy in various fields, such as medicine and engineering.\n\n### Challenges and Concerns:\n- Ethical issues, such as bias in AI algorithms.\n- Job displacement due to automation.\n- Privacy concerns with data collection and surveillance.\n- The potential risks of creating highly autonomous systems.\n\nIn summary, AI is a transformative technology with the potential to revolutionize industries and improve lives, but it also requires careful consideration of its ethical and societal implications.' additional_kwargs={'refusal': None} response_metadata={'token_usage': {'completion_tokens': 610, 'prompt_tokens': 11, 'total_tokens': 621, 'completion_tokens_details': {'accepted_prediction_tokens': 0, 'audio_tokens': 0, 'reasoning_tokens': 0, 'rejected_prediction_tokens': 0}, 'prompt_tokens_details': {'audio_tokens': 0, 'cached_tokens': 0}}, 'model_name': 'gpt-4o-2024-11-20', 'system_fingerprint': 'fp_b705f0c291', 'prompt_filter_results': [{'prompt_index': 0, 'content_filter_results': {'hate': {'filtered': False, 'severity': 'safe'}, 'jailbreak': {'filtered': False, 'detected': False}, 'self_harm': {'filtered': False, 'severity': 'safe'}, 'sexual': {'filtered': False, 'severity': 'safe'}, 'violence': {'filtered': False, 'severity': 'safe'}}}], 'finish_reason': 'stop', 'logprobs': None, 'content_filter_results': {'hate': {'filtered': False, 'severity': 'safe'}, 'protected_material_code': {'filtered': False, 'detected': False}, 'protected_material_text': {'filtered': False, 'detected': False}, 'self_harm': {'filtered': False, 'severity': 'safe'}, 'sexual': {'filtered': False, 'severity': 'safe'}, 'violence': {'filtered': False, 'severity': 'safe'}}} id='run-fed56c6e-d50e-48d7-b1d4-f50db5be4a1e-0' usage_metadata={'input_tokens': 11, 'output_tokens': 610, 'total_tokens': 621, 'input_token_details': {'audio': 0, 'cache_read': 0}, 'output_token_details': {'audio': 0, 'reasoning': 0}}


# Agent: Retriever
## Final Answer:
AI, or **Artificial Intelligence**, refers to the simulation of human intelligence in machines that are programmed to think, learn, and make decisions like humans. These systems are designed to perform tasks that typically require human intelligence, such as problem-solving, understanding natural language, recognizing patterns, and adapting to new information.

### Key Components of AI:
1. **Machine Learning (ML):**
   - A subset of AI that enables machines to learn from data and improve their performance over time without being explicitly programmed.
   - Example: A recommendation system on Netflix or Amazon.

2. **Natural Language Processing (NLP):**
   - The ability of machines to understand, interpret, and respond to human language.
   - Example: Virtual assistants like Siri, Alexa, or chatbots.

3. **Computer Vision:**
   - The ability of machines to interpret and analyze visual data, such as images or videos.
   - Example: Facial recognition or object detection.

4. **Robotics:**
   - The use of AI to control robots that can perform tasks autonomously or semi-autonomously.
   - Example: Self-driving cars or robotic arms in manufacturing.

Part 6: Challenges in Deploying Agentic RAG

While the benefits are substantial, implementing Agentic RAG is not without its challenges:

Data Quality and Consistency: The system’s performance is highly dependent on the quality and consistency of the underlying data. Inconsistent or outdated data can lead to inaccuracies.
Integration Complexity: Seamlessly integrating multiple agents, tools, and external data sources requires careful design and robust infrastructure.
Computational Resources: Multi-step reasoning and dynamic retrieval can be resource-intensive, especially when processing real-time data or deploying at scale.
Ethical and Bias Considerations: As with all AI systems, ensuring fairness and mitigating biases in the training data are critical to maintaining trust.
Ongoing Maintenance: Agentic RAG systems require continuous updates and maintenance to stay current with new data and evolving user needs.

Addressing these challenges involves adopting best practices in data management, investing in scalable infrastructure, and implementing robust feedback loops to refine the system over time.

The Future of Agentic RAG

The evolution of Agentic RAG is poised to redefine how we interact with information. Emerging trends indicate several exciting directions:

Multimodal Capabilities: Future systems may integrate text, images, audio, and video data, enabling richer context and more immersive user experiences.
Personalization: Leveraging user profiles and interaction histories, Agentic RAG can deliver hyper-personalized responses tailored to individual needs.
Enhanced Explainability: As users demand more transparency, future systems will provide clearer explanations of how responses are generated, building trust and accountability.
Integration with Edge Computing: Deploying Agentic RAG models closer to the data source (e.g., on mobile devices or local servers) can reduce latency and improve responsiveness.
Industry-Specific Solutions: Customized Agentic RAG applications for sectors like healthcare, finance, and legal services will become increasingly prevalent, offering specialized insights and support.

These advancements will further blur the lines between information retrieval, decision-making, and autonomous action, paving the way for AI systems that are not only intelligent but also deeply integrated into our everyday workflows.