Feb 24
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Are you tired of jumping across dozens of websites to find credible AI research papers, academic manuscripts, or technical articles? In this vlog, I walk you through building a real-world AI Research Assistant Agent from scratch using CrewAI, GPT-5.2, and Streamlit—designed to work even in restricted enterprise environments. This is not a toy demo!! You’ll learn how to design agentic workflows the right way, with clean architecture, traceability, and extensibility—just like a real research team. 🔑 Key Takeaways How to design a multi-agent research pipeline (Researcher → Summarizer) Why agent separation is critical to reduce hallucinations How CrewAI acts as an orchestrator, not just a wrapper Designing agent workflows that work in locked-down enterprise networks A future-proof project structure you can deploy, automate, or extend How to generate executive-ready research reports with citations Applying the same architecture to academic research & enterprise workflows 🧠 What You’ll Learn ✔️ End-to-end agent architecture ✔️ CrewAI agent roles, goals & backstories ✔️ Tool-driven web research without heavy infrastructure ✔️ Secure environment setup (API keys, secrets, GitHub safety) ✔️ Streamlit UI as a control layer (not the brain) ✔️ Real research use cases: Agentic AI & Attention Mechanisms ⏱️ Chapters & Timeline 00:00 – Intro: Why researchers need AI agents 01:10 – What we’re building (real research assistant, not a demo) 02:25 – High-level agent architecture (Researcher vs Summarizer) 04:50 – Project structure & why it matters 06:50 – Python setup & virtual environments 08:30 – Installing dependencies & libraries 10:00 – Design choices: no vector DB, no paid APIs (yet) 12:25 – Environment variables & API key security 14:50 – Tools layer: Wikipedia search, HTML parsing, file writing 17:15 – Defining agents with CrewAI (roles, goals, backstories) 19:40 – Streamlit UI walkthrough 20:35 – Live demo: Enterprise agentic AI research 24:45 – Live demo: Academic research (Attention Mechanisms) 28:55 – Results, citations & executive summaries 33:10 – Next steps: MCP servers, n8n, automation & scaling 34:30 – Wrap-up & community call-to-action 🧩 Tech Stack Used CrewAI – Multi-agent orchestration GPT-5.2 – Reasoning engine Streamlit – UI & control layer Python 3.12 – Core runtime BeautifulSoup + Requests – Document parsing Modular agent tools & prompts 📂 GitHub Repository 🔗 Code & Project Structure: 👉 https://github.com/soudey123/AIAgentLab/tree/main/Research%20Assistant%20Agent (Will include full source code, prompts, tools, and setup instructions) 🚀 Who This Video Is For AI Engineers & ML Practitioners Academic Researchers Data Scientists Enterprise AI Architects Anyone building agentic workflows that actually ship 💬 What’s Next? In future episodes, I’ll show you how to: Convert this into an MCP server Integrate with n8n / Zapier Add vector databases (ChromaDB, Pinecone) Automate daily research summaries 👉 Drop your use cases in the comments. 👍 Like if this helped 💬 Comment with your agent ideas 🔔 Subscribe for more real-world AI builds — Sam | Data Science with Sam #aiagents #podcast #artificialintelligence #datasciencewithsam #technicalvlog