Course Objectives
1. Why should I learn AI Agents with AutoGen and CrewAI?
AI Agents are transforming how businesses automate complex tasks using Large Language Models. Learning AutoGen and CrewAI enables you to build intelligent systems that collaborate, reason, and complete multi-step workflows with minimal human intervention.
2. What practical knowledge will I gain from this course?
You will learn to build autonomous AI agents, integrate Large Language Models, develop multi-agent workflows, connect external APIs, implement memory systems, use Retrieval-Augmented Generation (RAG), and deploy enterprise-ready AI applications. Practical projects provide hands-on experience with modern AI agent frameworks.
3. How does this course support my AI career?
Organizations are increasingly adopting AI Agents to automate customer support, software development, research, HR, finance, healthcare, and enterprise operations. This course equips you with practical skills aligned with current Generative AI and Intelligent Automation industry requirements.
4. What will I achieve after completing this training?
After completing this course, you will be able to design, build, deploy, and optimize autonomous AI agent applications using AutoGen, CrewAI, LangGraph, and modern LLM technologies. You will also develop a professional portfolio demonstrating real-world enterprise AI solutions.
Popular Techniques Covered in This Course
Python Programming
AI Agent Development
AutoGen Framework
CrewAI Framework
LangGraph Workflows
Prompt Engineering
Multi-Agent Systems
Tool Calling
RAG Integration
Enterprise AI Deployment
Get Hands-on Knowledge about Real-Time Projects
Project 1
Autonomous Research Agent
Build an AI research assistant that can search the web, summarize information, compare sources, generate insights, and create structured reports using AutoGen and Large Language Models. This project demonstrates autonomous reasoning and multi-step task execution.
Project 2
Multi-Agent Business Automation System
Develop a CrewAI-based workflow where multiple AI agents collaborate to handle tasks such as requirement analysis, email drafting, meeting summarization, task prioritization, and report generation. Learners gain practical experience in multi-agent orchestration and business process automation.
Project 3
Enterprise Knowledge Assistant
Create an AI-powered knowledge assistant that integrates Retrieval-Augmented Generation (RAG), vector databases, and enterprise documents to answer employee queries with accurate, context-aware responses. The project focuses on enterprise search and knowledge management automation.
Project 4
Intelligent Customer Support Agent
Design an autonomous customer support system capable of understanding user intent, retrieving relevant information, escalating issues when necessary, and maintaining conversation memory across multiple interactions. This project combines AI agents, memory systems, and tool integrations for real-world deployment.
Key Features
Edubrights offers AI Agents – AutoGen & CrewAI Training in virtual mode with expert trainers.Here are the Key Features,
40 Hours Course Duration
100% Job Oriented Training
Industry Expert Faculties
Free Demo Class Available
Completed 500+ batches
Certification Guidance
Curriculum
Module 1: Introduction to AI Agents & Generative AI
- Artificial Intelligence Fundamentals
- Generative AI Overview
- Large Language Models (LLMs)
- AI Agents Architecture
- Autonomous AI Systems
- Enterprise AI Applications
Module 2: Python for AI Agent Development
- Python Fundamentals
- Virtual Environments
- API Integration
- JSON Processing
- Asynchronous Programming
- Development Environment Setup
Module 3: Large Language Models & Prompt Engineering
- OpenAI Models
- Hugging Face Models
- Local LLMs
- Prompt Engineering
- Context Windows
- System Prompts
Module 4: AutoGen Framework
- AutoGen Architecture
- Conversational Agents
- Multi-Agent Collaboration
- Agent Communication
- Task Execution
- Workflow Automation
Module 5: CrewAI Framework
- CrewAI Fundamentals
- Roles & Responsibilities
- Agents & Crews
- Sequential Workflows
- Parallel Execution
- Task Management
Module 6: LangGraph & AI Workflows
- LangGraph Basics
- State Management
- Graph-Based Workflows
- Conditional Routing
- Agent Orchestration
- Workflow Optimization
Module 7: Memory & Knowledge Management
- Short-Term Memory
- Long-Term Memory
- Conversation History
- Vector Databases
- Semantic Search
- Knowledge Retrieval
Module 8: Tool Calling & External Integrations
- Function Calling
- API Integration
- Database Connectivity
- Web Search Integration
- Python Tool Execution
- Enterprise Connectors
Module 9: RAG & Enterprise AI Agents
- Retrieval-Augmented Generation
- Document Processing
- Embeddings
- Vector Databases
- Knowledge Base Integration
- Intelligent Search
Module 10: AI Agent Deployment
- FastAPI
- Docker
- Cloud Deployment
- REST APIs
- Monitoring
- Security Best Practices
Module 11: Enterprise AI Automation
- Customer Support Agents
- HR Automation
- Research Assistants
- Document Intelligence
- Workflow Automation
- AI Governance
Module 12: Capstone Projects
- AI Research Assistant
- Multi-Agent Business Automation
- Enterprise Knowledge Assistant
- Autonomous Customer Support Agent
- AI Workflow Orchestration
- Final Project Presentation
Receive Training From Our Skilled and Effective Trainers
Experience in the Industry
Gain expertise from experienced AI engineers and machine learning specialists who have developed, fine-tuned, and deployed Large Language Models (LLMs) for enterprise AI applications, intelligent assistants, and domain-specific solutions.
Backgrounds at the Top
Our LLM Fine-Tuning trainers have worked on transformer-based models, instruction tuning, parameter-efficient fine-tuning (LoRA/QLoRA), Retrieval-Augmented Generation (RAG), and production AI deployments across technology companies, research organisations, and enterprise AI teams.
Clear & Effective Teaching
LLM architecture, dataset preparation, prompt engineering, supervised fine-tuning, reinforcement learning concepts, model evaluation, and deployment strategies are explained clearly with practical examples and real-world AI use cases.
Hands-On Learning Focus
Students prepare training datasets, fine-tune open-source LLMs, implement LoRA and QLoRA techniques, evaluate model performance, integrate vector databases, and deploy customised AI models through structured hands-on lab exercises.
Up-to-Date Knowledge
Trainers keep content current with the latest advancements in open-source LLMs, Hugging Face, PEFT, quantization, Retrieval-Augmented Generation (RAG) frameworks, AI deployment tools, and evolving generative AI best practices.
About Our Course Certification*
The AI Agents – AutoGen & CrewAI Course in Chennai is designed to help learners master the development of autonomous AI systems using the latest multi-agent frameworks and Large Language Model technologies. This course provides practical training in AutoGen, CrewAI, LangGraph, RAG integration, memory management, tool calling, workflow orchestration, and enterprise AI deployment through hands-on projects and real-world business use cases.
Course FAQs
1. What are AI Agents?
AI Agents are intelligent software systems that use Large Language Models (LLMs) to understand goals, make decisions, use external tools, retrieve information, and complete tasks autonomously. Modern AI Agents can collaborate with other agents, execute workflows, access APIs, and automate complex business processes with minimal human intervention.
2. Who can enroll in the AI Agents – AutoGen & CrewAI Course?
This course is ideal for students, fresh graduates, Python developers, software engineers, AI enthusiasts, machine learning professionals, data scientists, cloud engineers, DevOps professionals, and working professionals who want to build autonomous AI applications. Beginners with basic Python knowledge can also follow the course through structured practical sessions.
3. Are there any prerequisites for learning AI Agents?
Basic Python programming knowledge is recommended, but prior experience in Artificial Intelligence or Machine Learning is not mandatory. The course starts with Generative AI fundamentals before introducing Large Language Models, AutoGen, CrewAI, LangGraph, RAG, memory systems, and enterprise AI agent development.
4. How long does it take to complete the AI Agents training?
The course duration depends on the selected learning mode. Most learners complete the program over several weeks through instructor-led classes, practical labs, assignments, enterprise AI case studies, and capstone projects designed to strengthen real-world implementation skills.
5. Does the course include practical projects?
Yes. The training is highly practical and includes projects such as AI research assistants, autonomous customer support agents, multi-agent business automation systems, enterprise knowledge assistants, workflow orchestration platforms, and intelligent document processing applications.
6. Which tools and technologies will I learn?
You will gain hands-on experience with Python, AutoGen, CrewAI, LangGraph, LangChain, OpenAI APIs, Hugging Face Transformers, MCP (Model Context Protocol), FastAPI, Docker, vector databases, Pinecone, ChromaDB, FAISS, Retrieval-Augmented Generation (RAG), and cloud deployment concepts used in enterprise AI development.
7. What job roles can I pursue after completing this course?
After completing the course, learners can explore opportunities as AI Engineer, Generative AI Developer, AI Agent Developer, LLM Engineer, Machine Learning Engineer, NLP Engineer, Python AI Developer, Enterprise AI Consultant, AI Solutions Engineer, and Intelligent Automation Developer.
8. Are AI Agents in demand?
Yes. AI Agents are becoming one of the fastest-growing areas in Generative AI. Organizations are adopting autonomous AI systems for customer service, research, software development, workflow automation, document intelligence, HR, finance, healthcare, and enterprise operations, creating strong demand for professionals with AI Agent development skills.
9. Is this course suitable for beginners?
Yes. The course is designed with a beginner-friendly learning approach. It starts with AI fundamentals, Python programming, and Large Language Models before progressing to AutoGen, CrewAI, LangGraph, multi-agent collaboration, memory management, and enterprise AI deployment through guided hands-on projects.
10. Why choose AI Agents – AutoGen & CrewAI Training in Chennai?
Chennai has become an important technology hub with increasing adoption of Artificial Intelligence across industries. This training provides practical experience with modern AI Agent frameworks, enterprise automation projects, and industry-relevant tools that prepare learners for real-world AI development environments.
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