Building an Agentic RAG System: Smarter Answers for a Complex World
Apoorva ShuklaWhy Agentic RAG? And Why Now?
Vanilla Retrieval-Augmented Generation (RAG) systems work like diligent librarians: you ask a question, they fetch documents from a knowledge base, and generate a response. But here’s the catch—they rarely ask follow-ups, second-guess their choices, or suggest a better way to phrase your question. And that’s where they fall short, especially with nuanced topics like international trade, law, or policy.
Agentic RAG, by contrast, brings a whole new level of intelligence and flexibility into the loop. Imagine instead of a single, silent librarian, you have a team: one reads the question, another pulls info from shelves, someone else checks if that info even makes sense, and if not, someone rewrites the question more clearly before starting over. It's not just smart—it's collaborative, recursive, and dynamic.
Key Differences
| Feature | Vanilla RAG | Agentic RAG |
| Query understanding | Single pass | Adaptive, with rewrites if needed |
| Relevancy evaluation | Often basic or manual tuning | Automated with LLM-based grading |
| Feedback loop | None | Decision loops built into flow |
| Control logic | Predefined retrieval → generation pipeline | Agent-driven decisions with conditional flows |
| Best use case | Factual, well-defined queries | Open-ended, ambiguous, or complex real-world tasks |
The Inspiration
I built this project out of an itch to go beyond traditional RAG demos. I wanted to see what would happen if a system could reason like a team—if it could pause, evaluate, and improve itself in real time. I chose a subject that is notoriously dense and dynamic: global trade, particularly involving US-China and US-India relations. This allowed me to test the system's ability to deal with politically charged, evolving narratives.
Architecture Overview
1. LLM Decision Maker
The orchestrator. It reads the user query and decides whether the system should retrieve data or try reframing the question.
2. Vector Data Retriever (ToolNode)
Connects to a custom vector store filled with trade-related documents:
Recent details about the US-China trade war
The signed US-China trade deal
The upcoming US-India trade deal negotiations
It retrieves documents semantically aligned to the user query.
3. Grader Function
This is the critical thinker. It uses an LLM to evaluate whether the retrieved documents are relevant enough to proceed. If not, the system doesn’t settle—it course corrects.
If the response is "YES", it moves to generation.
If "NO", the system rewrites the query for better alignment.
4. Query Rewriter Node
This node revisits the user question and smartly rephrases it to improve context alignment. It then loops the query back into the Decision Maker.
5. Generator Node
The final node. It uses both the original (or rewritten) query and the most relevant document snippets to generate a clear, factual response.
And once the response is produced—the loop ends!
Technology Behind the Curtain
This project wouldn’t have been possible without these building blocks:
LangGraph – for graph-based agent orchestration
LangChain – to plug LLMs into tool nodes for grading, rewriting, and generation
Vector Store – holds embedded representations of policy docs
LLM Backbone – any robust language model can power the agent's core reasoning
Real-World Relevance: Why Trade Documents?
Trade policy is a treasure trove of ambiguity. A single clause in a document can have massive economic consequences. With the US-China trade tensions and the US-India trade deal on the horizon, the data is ripe for extraction—but only if done wisely.
This Agentic RAG architecture ensures that the system doesn’t bluff when it doesn't know. Instead, it rewrites, refines, and searches again until it’s confident in its answer.
What’s Possible Next?
This is just a starting point. With this framework in place, the potential extensions are endless:
Adding human feedback for reinforcement
Expanding to multiple domains like climate or health
Experimenting with open-source LLMs or fine-tuned agents
Explore the Code
Want to see the system in action, fork it?
https://github.com/apshuk21/Agentic-RAG
You’ll find clean code, a modular structure, and instructions to get your own trade-intelligent RAG agent running in minutes.
Final Thoughts
While traditional RAG systems feel like static bookshelves, Agentic RAG behaves more like a curious researcher—one who questions, adapts, and improves every step of the way. It doesn’t just answer. It investigates.
If you’re working on applications that require precision, adaptability, and accountability, adding agentic reasoning into your RAG setup could be a game-changer.
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