DEV Community: David Malick Dieng

Why Building AI Agents with UiPath is More Robust Than Using Low-Code Tools by David Malick Dieng

David Malick Dieng — Tue, 26 Aug 2025 15:08:10 +0000

🌍 The Rise of AI Agents in Automation

AI agents are no longer a futuristic concept — they are here, and they are transforming the way organizations work. From document processing to customer service, AI agents can reason, plan, and collaborate to achieve business outcomes.

But when it comes to implementing them in the real world, the question is:
👉 Should you use a lightweight low-code tool (like n8n, Make, or Zapier) or a full automation platform like UiPath?

The answer: If you want robustness, scalability, and enterprise readiness, UiPath is the way to go.

⚡ Low-Code Tools: Great for Prototypes, Limited for Scale

Platforms like Make, Zapier, and n8n are amazing for:

Quick proofs of concept

Connecting APIs together

Lightweight personal or small business automations

But they quickly show limitations when you try to:

Scale across departments

Ensure security & compliance

Orchestrate long-running processes

Integrate with enterprise systems (SAP, Oracle, Dynamics, etc.)

For AI agents, you need more than just API triggers. You need agentic orchestration: reasoning, memory, monitoring, and integration at scale.

🏆 Why UiPath is More Robust for AI Agents

Here’s why UiPath stands out compared to typical low-code tools:

Enterprise-Grade Orchestration

UiPath Orchestrator provides centralized control, monitoring, and scaling of agents — something low-code tools lack. You can deploy agents across thousands of users, track performance, and manage workloads.

Native AI Integration

UiPath has AI Center and Document Understanding, which allow direct integration of machine learning models, LLMs, and now AI Agents. This means your agent doesn’t just run scripts; it can reason, classify, extract, and make decisions in a controlled environment.

Security & Compliance

Enterprises care about audit logs, governance, GDPR, and ISO certifications. UiPath is built with this in mind, while most low-code tools operate at a “best effort” security level.

Scalable Multi-Agent Collaboration

With UiPath, you can create an ecosystem of agents — one handling customer queries, another handling invoices, another updating ERP systems — all orchestrated seamlessly. Low-code tools struggle once workflows become interdependent.

Native Integration with Legacy + Modern Systems

Unlike low-code tools that mostly connect to APIs, UiPath can automate desktop apps, Citrix, SAP, mainframes, and non-API systems. This is critical in large organizations where legacy still runs the core business.

Hybrid Human + AI Workflows

UiPath enables human-in-the-loop setups, where AI agents handle 80% of a task and humans step in for the exceptions. This is essential for compliance-heavy industries (finance, insurance, healthcare).

🚀 Example: AI Agent in UiPath vs. Low-Code

Low-Code Setup: You build a chatbot with Make + OpenAI → it can answer FAQs, but struggles when asked to fetch data from SAP or process PDFs securely.

UiPath Setup: You build an AI agent in UiPath → it retrieves data from SAP, processes invoices with Document Understanding, and escalates exceptions to humans — all under enterprise-grade monitoring.

Result: UiPath agents are not just chatbots — they’re full digital coworkers.

🔮 The Future: UiPath Agentic Automation

UiPath is moving towards Agentic Automation — giving agents memory, reasoning, and the ability to collaborate. Instead of automating only repetitive tasks, UiPath AI Agents will:

Understand business goals

Break them into subtasks

Collaborate with other agents & humans

Deliver measurable outcomes

This is beyond what lightweight low-code platforms can achieve.

💡 Conclusion

Low-code tools are fantastic for experiments and demos, but if you’re building AI agents that must be secure, scalable, and enterprise-ready, UiPath is the most robust platform.

It combines:
✅ AI + Automation + Orchestration
✅ Security & Governance
✅ Multi-agent collaboration
✅ Human-in-the-loop workflows

In short: If you want AI agents to play a real role in your organization — not just in side projects — UiPath is the way to go.

👉 Follow me for more insights on AI Agents, Automation, and Digital Transformation.

How to Build a Strong RAG Agent (Step-by-Step Guide) by David Malick Dieng

David Malick Dieng — Tue, 26 Aug 2025 14:58:40 +0000

Introduction

Large Language Models (LLMs) are powerful, but they come with a well-known limitation: hallucinations — confidently making things up.

That’s where Retrieval-Augmented Generation (RAG) comes in. By connecting an LLM to an external knowledge base, we can ground its answers in real data.

In this article, I’ll show you how to build a strong RAG agent from scratch, explain the key components, and share best practices to make it production-ready. By the end, you’ll have a working pipeline and a roadmap to scale it into multi-agent systems.

What is RAG?

RAG = Retriever + Generator

Retriever: Finds the most relevant chunks of information from a knowledge base (e.g., vector database).

Generator: Uses the LLM to generate an answer, using both the query + retrieved context.

Without RAG:

Q: “When was OpenAI founded?”
A: “In the 1980s by Steve Jobs.” (🤦 hallucination)

With RAG:

Q: “When was OpenAI founded?”
A: “OpenAI was founded in December 2015 by Sam Altman, Elon Musk, and others.”

📌 RAG ensures factual accuracy by grounding LLMs in external knowledge.

🛠️ Core Components of a Strong RAG Agent

To make your RAG agent robust, you need to get these pieces right:

Chunking → Split documents into meaningful, overlapping chunks (too big = missed context, too small = fragmented info).

Embeddings → Convert chunks into vector representations using models like OpenAI text-embedding-3-large or open-source all-MiniLM-L6-v2.

Vector Database → Store embeddings for fast semantic search (Pinecone, Weaviate, FAISS, Milvus).

Retriever → Finds top-k relevant chunks.

Generator (LLM) → Produces the final answer (OpenAI GPT-4, Claude, or LLaMA).

Orchestration → Frameworks like LangChain or LlamaIndex to connect it all.

Step-by-Step Implementation

We’ll build a minimal RAG pipeline using LangChain + FAISS.

pip install langchain openai faiss-cpu tiktoken

`from langchain.embeddings import OpenAIEmbeddings
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.vectorstores import FAISS
from langchain.llms import OpenAI
from langchain.chains import RetrievalQA

1. Load documents (example text)

docs = [
"OpenAI was founded in December 2015 by Sam Altman, Elon Musk, Greg Brockman, Ilya Sutskever, and Wojciech Zaremba.",
"RAG stands for Retrieval-Augmented Generation. It combines external knowledge with LLMs."
]

2. Split documents into chunks

splitter = RecursiveCharacterTextSplitter(chunk_size=200, chunk_overlap=50)
documents = splitter.create_documents(docs)

3. Create embeddings + store in FAISS

embeddings = OpenAIEmbeddings()
vectorstore = FAISS.from_documents(documents, embeddings)

4. Create retriever

retriever = vectorstore.as_retriever(search_kwargs={"k": 2})

5. Build RAG pipeline (Retriever + Generator)

qa = RetrievalQA.from_chain_type(
llm=OpenAI(model="gpt-3.5-turbo"),
retriever=retriever
)

6. Query the RAG agent

query = "Who founded OpenAI?"
result = qa.run(query)
print(result)
`

This simple RAG agent retrieves relevant info and feeds it to GPT for accurate answers.

Best Practices for a “Strong” RAG Agent

Optimize chunk size (200–500 tokens with 10–20% overlap).

Hybrid search → Combine semantic + keyword search for better recall.

Metadata filtering → Tag docs with source, date, etc., and filter by context.

Evaluate regularly → Use frameworks like LangSmith
to measure hallucinations & accuracy.

Cache results for repeated queries (e.g., Redis).

🤖 Multi-Agent RAG Collaboration

A single RAG agent is powerful, but the future is multi-agent systems:

Research Agent → Finds data.

Summarizer Agent → Compresses info.

QA Agent → Delivers the final polished answer.

Together, they act like a team of specialists, each grounded in the same RAG pipeline.

Example use case:
📚 AI Tutors → one agent finds knowledge, another explains it, another checks correctness.

📂 Resources & Next Steps

🔗 LangChain Docs

🔗 LlamaIndex

🔗 Awesome RAG GitHub