Title of the talk/workshop
Building Production-Grade Agentic AI Systems with RAG and Multi-Agent Workflows
Abstract of the talk/workshop
Generative AI is rapidly moving from prototypes to production systems, but building reliable enterprise-grade AI applications requires more than just prompting an LLM. In this session, we will explore how to design and deploy production-ready Agentic AI systems using RAG (Retrieval-Augmented Generation), multi-agent orchestration, vector databases, and LLM frameworks.
The talk will cover practical architecture patterns for Supervisor + Child agents, retrieval pipelines, hallucination handling, prompt optimization, observability, and human-in-the-loop workflows. Real-world enterprise examples from document intelligence and workflow automation will be discussed to demonstrate how Agentic AI can be safely deployed at scale.
Attendees will gain practical insights into LangChain, LangGraph, CrewAI, vector databases, and production AI system design.
Category of the talk/workshop
Data Science, Machine Learning, and AI
Duration (including Q&A)
45 Minutes (35 min talk + 10 min Q&A)
Level of Audience
Intermediate
Speaker Bio
Sridhar S is an AI Engineer with 3+ years of experience in AI/ML, Generative AI, and Agentic AI systems. He has worked on production-grade RAG pipelines, multi-agent architectures, intelligent document processing, and enterprise AI automation using technologies such as LangChain, LangGraph, Azure OpenAI, vector databases, and LLMs.
Company: Bradsol / AI Engineer
Email: [sridharsukumar2001@gmail.com](mailto:sridharsukumar2001@gmail.com)
Years of Experience: 3+
Prerequisites (if any)
Basic understanding of Python and Generative AI concepts would be helpful. No mandatory setup required.
Title of the talk/workshop
Building Production-Grade Agentic AI Systems with RAG and Multi-Agent Workflows
Abstract of the talk/workshop
Generative AI is rapidly moving from prototypes to production systems, but building reliable enterprise-grade AI applications requires more than just prompting an LLM. In this session, we will explore how to design and deploy production-ready Agentic AI systems using RAG (Retrieval-Augmented Generation), multi-agent orchestration, vector databases, and LLM frameworks.
The talk will cover practical architecture patterns for Supervisor + Child agents, retrieval pipelines, hallucination handling, prompt optimization, observability, and human-in-the-loop workflows. Real-world enterprise examples from document intelligence and workflow automation will be discussed to demonstrate how Agentic AI can be safely deployed at scale.
Attendees will gain practical insights into LangChain, LangGraph, CrewAI, vector databases, and production AI system design.
Category of the talk/workshop
Data Science, Machine Learning, and AI
Duration (including Q&A)
45 Minutes (35 min talk + 10 min Q&A)
Level of Audience
Intermediate
Speaker Bio
Sridhar S is an AI Engineer with 3+ years of experience in AI/ML, Generative AI, and Agentic AI systems. He has worked on production-grade RAG pipelines, multi-agent architectures, intelligent document processing, and enterprise AI automation using technologies such as LangChain, LangGraph, Azure OpenAI, vector databases, and LLMs.
Company: Bradsol / AI Engineer
Email: [sridharsukumar2001@gmail.com](mailto:sridharsukumar2001@gmail.com)
Years of Experience: 3+
Prerequisites (if any)
Basic understanding of Python and Generative AI concepts would be helpful. No mandatory setup required.