Agentic AI Training Course - Master AI Agents

Agentic AI refers to artificial intelligence systems that can autonomously make decisions, take actions, and pursue goals with minimal human intervention. Unlike traditional AI, which adheres to predetermined rules, agentic AI may dynamically adapt to new situations. It is widely utilized in robotics, virtual assistants, self-driving cars, and sophisticated decision-making processes.

Learning Agentic AI is essential for building autonomous systems that make intelligent decisions, adapt to changes, and optimize tasks with minimal human input. It drives advancements in robotics, automation, and AI-powered assistants, opening career opportunities in AI research and development. As AI grows more independent, understanding its ethical and safety implications becomes crucial, making expertise in this field highly valuable for the future.

Examples of Agentic AI include AutoGPT and BabyAGI, which autonomously generate and execute tasks. CrewAI enables AI agents to collaborate on complex workflows, while LangGraph Agents use stateful memory for decision-making. Autogen facilitates multi-agent interactions with human oversight. LlamaIndex enhances document retrieval in AI workflows, while Pinecone, Weaviate, and ChromaDB serve as vector databases for efficient AI memory and knowledge retrieval.

Agentic AI focuses on autonomous decision-making, reasoning, and action execution, enabling AI agents to operate independently and collaborate dynamically. In contrast, Generative AI specializes in creating content, such as text, images, or code, based on learned patterns. While Generative AI generates outputs from prompts, agentic AI actively engages with environments, utilizes tools, plans actions, and adapts over time, making it more interactive and goal-driven than purely content-generating AI.

Yes, learning Agentic AI is likely worth it, as a rapidly growing field with significant potential to revolutionise how businesses operate by automating complex tasks, enabling autonomous decision-making, and facilitating seamless human-AI collaboration, it is regarded as a valuable skill for the future of work across a wide range of industries.

In order to complete this course successfully, participants need to have a basic understanding of Python programming language, machine learning, deep learning, natural language processing, generative AI, and prompt engineering concepts. However, learners will be provided with self-learning refresher material on generative AI and prompt before beginning with this live classes of the AI agents course.

Becoming an agentic AI engineer is beneficial for the participant as it is transforming the business operations, enhancing productivity, and redefining innovation.

Upon completing the agentic AI Training course, participants will learn to design and build autonomous AI agents using frameworks like LangChain, LangGraph, and CrewAI. They will implement agentic RAG for intelligent retrieval, develop multi-agent systems, and integrate Autogen for adaptive AI workflows. The course covers AI observability with LangFuse, no-code AI development, and cloud deployment on AWS, Azure, and GCP. Hands-on projects ensure practical expertise in building scalable, real-world AI solutions.

The Agentic AI Training Course is ideal for AI enthusiasts, developers, and professionals looking to build autonomous AI agents. It is best suited for LLM Engineer , Generative AI Engineer , AI Research scientists, AI/ML practitioners, and freshers who want to leverage Agentic AI for automation and decision-making.

Yes, Agentic AI is shaping the future of AI by enabling autonomous, goal-driven systems that can reason, plan, and act independently. Unlike traditional AI, which follows predefined rules, Agentic AI dynamically adapts, collaborates, and interacts with its environment. As industries move toward automation, intelligent decision-making, and multi-agent collaboration, Agentic AI will play a crucial role in revolutionizing business processes, AI-powered assistants, robotics, and real-world problem-solving.

Yes, becoming an AI Agent Engineer is a very promising career path due to the rapidly growing field of agentic AI, which is viewed as a significant technological advancement with enormous potential across industries, making it a high-demand area for skilled professionals with the ability to build and deploy autonomous AI systems.

A step-by-step guide for setting up environments and building AI agents will be provided in the Learning Management System (LMS). Edureka's Support Team will be available 24/7 to assist you in case you have any questions or face any technical issues during the practicals.

To learn agentic AI as a beginner, start by building a foundation in AI concepts like Python programming, machine learning and reinforcement learning, deep learning, NLP, generative AI, and prompt engineering. Gradually explore key agentic AI principles, including autonomy, goal-oriented behavior, and tool use. Hands-on experience with frameworks like LangChain, LangGraph, and OpenAI Gym will enhance learning. Completing real-world projects will help develop practical skills in building intelligent AI agents.

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