Agentic RAG: Revolutionizing AI Workflows in 2025

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Agentic RAG (Retrieval-Augmented Generation) combines the power of AI agents with RAG to create dynamic, intelligent workflows. Companies like Glean AI, Perplexity, and Harvey use multi-agent RAG systems to enhance enterprise solutions.

How Agentic RAG Works

RAG (Retrieval-Augmented Generation)

  1. Query and Embedding (Retrieval) – The model fetches relevant data from sources, and a Vector DB extracts embeddings.
  2. Prompt Addition (Augmentation) – Retrieved data merges with the query and system prompt.
  3. LLM Output (Generation) – The Large Language Model (LLM) processes the combined input to generate a response.

AI Agents

  1. Query Handling – The agent analyzes the user query.
  2. Memory & Planning – Uses frameworks like ReACT or Reflexion to strategize responses.
  3. Tool Usage – Fetches real-time data via APIs (Google, Mail, etc.).
  4. Output Generation – Enhances responses using gathered data and reasoning.

Combining RAG & AI Agents

  • AI agents dynamically select data sources based on queries.
  • They refine RAG’s prompts with planning and real-time tool data.

Operational Workflow

  1. Query Routing – The agent processes and coordinates the query.
  2. Context Retention – Uses short/long-term memory for context.
  3. Task Planning – Defines retrieval strategy and selects tools.
  4. Data Fetching – Uses vector search to gather relevant data.
  5. Prompt Optimization – Combines retrieved data with reasoning for LLM input.
  6. Response Generation – LLM delivers the final output.

🔗 References:

You Should Know: Practical AI & Linux Commands for Agentic RAG

1. Setting Up a Vector Database (ChromaDB Example)

 Install ChromaDB 
pip install chromadb

Initialize and query 
import chromadb 
client = chromadb.Client() 
collection = client.create_collection("knowledge_base") 
collection.add(documents=["AI agents enhance RAG workflows"], ids=["doc1"]) 
results = collection.query(query_texts=["How do AI agents help RAG?"], n_results=1)

2. Running an AI Agent with LangChain

pip install langchain openai

from langchain.agents import load_tools, initialize_agent 
from langchain.llms import OpenAI

llm = OpenAI(temperature=0) 
tools = load_tools(["google-search"], llm=llm) 
agent = initialize_agent(tools, llm, agent="zero-shot-react-description") 
agent.run("What’s the latest research on Agentic RAG?")

3. Automating Retrieval with Python

from sentence_transformers import SentenceTransformer 
model = SentenceTransformer('all-MiniLM-L6-v2') 
query_embedding = model.encode("How does Agentic RAG work?")
  1. Linux Data Processing (jq, grep, awk for Logs)
    Filter AI agent logs 
cat agent_logs.json | jq '.transactions[] | select(.query == "RAG")'

Extract relevant API calls 
grep "API_CALL" debug.log | awk '{print $3, $5}'

5. Windows PowerShell for AI Workflows

 Check running AI processes 
Get-Process | Where-Object { $_.ProcessName -like "python" }

Monitor API calls 
Invoke-WebRequest -Uri "https://api.agentic-rag.com/query" -Method POST -Body '{"query":"AI trends 2025"}'

What Undercode Say

Agentic RAG is transforming AI workflows by integrating dynamic retrieval, real-time data, and reasoning. Enterprises leveraging this will outperform competitors in automation and decision-making.

Prediction

By 2026, 70% of AI-driven businesses will adopt Agentic RAG for complex workflows, reducing manual intervention by 50%.

Expected Output:

  • A functional AI agent with RAG integration.
  • Automated data retrieval and response generation.
  • Optimized enterprise workflows using dynamic AI planning.

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