From Zero to Production: Building Multi-Agent Systems with CrewAI

Registrations
Kindly note: Registrations for the event will be confirmed only if you register on the link here https://sahaj.ai/events/from-zero-to-production:-building-multi-agent-systems-with-crewai/

Workshop Overview
Learn to build a practical, business-ready AI agent system in just 2 hours! In this hands-on workshop, you’ll create a customer support system.
This multi-agent customer support system can classify, route, and respond to support tickets automatically using the power of Large Language Models and crewAI.

What You’ll Build
Transform your customer support workflow by building a multi-agent system that:

• Automatically classifies incoming support tickets
• Intelligently routes issues to the right department
• Generates contextual responses using your knowledge base
• Maintains conversation history for better customer experience

Tools You Will Explore

• CrewAI framework
• LLMs
• Basic database for ticket management

Who Should Attend

• Those interested in practical AI applications
• Those exploring automation solutions for their business
• Anyone interested in building real-world AI agents

Prerequisites

• Basic Python knowledge
• Familiarity with REST APIs
• Understanding of basic software architecture
• No prior AI/ML experience required!

What You’ll Learn

• Setting up and configuring AI agents with crewAI
• Implementing inter-agent communication
• Building robust classification systems using LLMs
• Managing conversation context and memory
• Best practices for production-ready AI agents

Meet our Speaker(s)
Keshav Rathinavel
Solution Consultant, Sahaj AI Software
Full-Stack Software Developer with a strong focus on building production-ready solutions. Keshav's expertise lies in leveraging AI to create innovative applications, with understanding of DevOps practices, to optimise development workflows and streamline deployment processes.

Preethi Srinivasan
Solution Consultant, Sahaj AI Software
Preethi Srinivasan is a Solution Consultant at Sahaj Software. She has a Masters (by Research) from IIT Mandi. As part of her masters thesis she worked on applications of Deep Learning algorithms to medical imaging problems and published work at NIPS-WIML workshop, IEEE-CBMS-20, ACCV-20. At Sahaj, she has developed prototypes for Video Summarisation (in an unsupervised fashion) and Video Captioning, focusing on extracting meaningful information from the video data. She is currently working on building question-answering systems for specific domains based on document analysis, utilising RAG and/or fine-tuning on LLMs.