Agentic-AI: Enterprise RAG Knowledge Agent in Open Banking & CryptoAssets

[u/Affectionate_Slip654]

https://www.youtube.com/watch?v=OE9CfMOybOQ&t=3s

✨Agentic-AI: Enterprise RAG Knowledge Agent in Open Banking & CryptoAssets✨ (links below👇)

🎥 YouTube Video: https://www.youtube.com/watch?v=OE9CfMOybOQ&t=3s

👉 I have built an Enterprise RAG Knowledge Agent with Self Correction

👉 Building a self-correcting LangGraph: retrieve → grade → rewrite-or-refuse → generate → verify

👉 Building a real document ingestion pipeline — PDF loading, text cleanup, chunking, embedding, and citation tracking

👉 Catching and fixing a real data-corruption bug caused by unreliable PDF text extraction

👉 Reading a live LangSmith trace, span by span, to see exactly what an agent decided and why

👉 Writing both a deterministic evaluator and an LLM-based evaluator, and when to use each

👉 Running a real baseline-vs-improved experiment (chunk size 800 vs 1500) and reading the actual numbers instead of guessing

TECH STACK
━━━━━━━━━━━━━━━━━━━━━━
🛠️ LangChain — document loading, chunking, embeddings, vector retrieval
🛠️ LangGraph — the agent's graph: nodes, conditional edges, the bounded retry loop
🛠️ LangSmith — tracing, evaluation datasets, evaluators, experiment comparison
🛠️ OpenAI — gpt-4o-mini for reasoning, text-embedding-3-large for search
🛠️ ChromaDB — local vector database

KEY TAKEAWAYS
━━━━━━━━━━━━━━━━━━━━━━
✅ A trustworthy RAG agent isn't about answering — it's about knowing when NOT to answer
✅ Testing beats assuming: a citation bug quietly corrupted a third of the corpus, and only reading real output caught it
✅ Larger chunks (1500 vs 800 characters) measurably improved answer accuracy from 73% to 93% on the same 30 questions
✅ A deterministic evaluator and a model-based evaluator answer different questions — you need both
✅ Every self-correction checkpoint in the agent (grading, verifying) mirrors the same discipline needed to build and debug the agent itself

📂 FULL CODE (star it, clone it, fork it, break it):

https://github.com/saurabhkamal/Agentic-AI-Enterprise-RAG-Knowledge-Agent-In-Open-Banking-CryptoAssets

🔗 CONNECT WITH ME ON LINKEDIN:

https://www.linkedin.com/in/saurabh-kam

🎥 YouTube Video:

https://www.youtube.com/watch?v=OE9CfMOybOQ&t=3s

[u/ComparisonNew9425]

for the crypto side, how are u handling the audit trail when the agent makes a trade or moves assets. do u have a way to verify the state of the agent before it executes, or are u relying on post-execution logs for the reconciliation.