Uploaded October 2025 | Updated September 2026, 2 weeks ago
This tutorial explores how to design vertical AI agents—domain-specific systems built for high-stakes environments like law, healthcare, and finance. Unlike general-purpose frameworks, vertical agents are built from the ground up for explainability, compliance, and precision. Speakers will cover core design patterns, reasoning loops, and retrieval strategies tailored for regulated contexts. The session includes real-world examples, a live walkthrough of a vertical RAG agent, and deployment insights using tools like vLLM, Ollama, and LangChain. Attendees will gain practical takeaways for building reliable, scalable agentic systems in critical domains
#ai #aiagents #agenticai #futureofdataandai #verticalagents #rag
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This tutorial explores how to design vertical AI agents—domain-specific systems built for high-stakes environments like law, healthcare, and finance. Unlike general-purpose frameworks, vertical agents are built from the ground up for explainability, compliance, and precision. Speakers will cover core design patterns, reasoning loops, and retrieval strategies tailored for regulated contexts. The session includes real-world examples, a live walkthrough of a vertical RAG agent, and deployment insights using tools like vLLM, Ollama, and LangChain. Attendees will gain practical takeaways for building reliable, scalable agentic systems in critical domains
#ai #aiagents #agenticai #futureofdataandai #verticalagents #rag
------
👉 Learn more about Data Science Dojo:
datasciencedojo.com
👉 Explore video tutorials:
datasciencedojo.com/tutorials
👉 See community feedback and success stories:
https://datasciencedojo.com/data-scie...
At Data Science Dojo, we believe data science is for everyone. Our in-person and virtual training programs have helped 10,000+ professionals from 2,500+ companies — including Microsoft, Apple, and Meta — apply AI responsibly and effectively.
🔗 Subscribe to our newsletter for more AI tutorials and events:
datasciencedojo.com/newsletter










