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Agentic AI with LLMs Course Overview

Agentic AI with LLMs Course Overview

Overview of the Agentic AI with LLMs Course

The Agentic AI with LLMs course at Koenig Solutions offers participants an engaging exploration of the innovative applications of Large Language Models (LLMs) in creating intelligent agents. Throughout the course, learners will gain a solid understanding of agent-oriented programming, harnessing the power of LLMs to develop autonomous agents capable of decision-making and communication.

Learning objectives include mastering the underlying principles of LLMs, implementing practical use cases, and evaluating performance metrics. By the end of the course, students will be equipped to apply their knowledge in real-world scenarios, enhancing their skills in AI-driven solutions and paving the way for future AI advancements. Join us to elevate your expertise in this cutting-edge field!

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850

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Course Fee 850
Total Fees
850 (USD)
  • Live Training (Duration : 16 Hours)
  • Per Participant
  • Guaranteed-to-Run (GTR)
  • Classroom Training fee on request
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Inclusions in Koenig's Learning Stack may vary as per policies of OEMs

  • Live Training (Duration : 16 Hours)
  • Per Participant
  • Classroom Training fee on request
Koeing Learning Stack

Koenig Learning Stack

Free Pre-requisite Training

Join a free session to assess your readiness for the course. This session will help you understand the course structure and evaluate your current knowledge level to start with confidence.

Assessments (Qubits)

Take assessments to measure your progress clearly. Koenig's Qubits assessments identify your strengths and areas for improvement, helping you focus effectively on your learning goals.

Post Training Reports

Receive comprehensive post-training reports summarizing your performance. These reports offer clear feedback and recommendations to help you confidently take the next steps in your learning journey.

Class Recordings

Get access to class recordings anytime. These recordings let you revisit key concepts and ensure you never miss important details, supporting your learning even after class ends.

Free Lab Extensions

Extend your lab time at no extra cost. With free lab extensions, you get additional practice to sharpen your skills, ensuring thorough understanding and mastery of practical tasks.

Free Revision Classes

Join our free revision classes to reinforce your learning. These classes revisit important topics, clarify doubts, and help solidify your understanding for better training outcomes.

Inclusions in Koenig's Learning Stack may vary as per policies of OEMs

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♱ Excluding VAT/GST

You can request classroom training in any city on any date by Requesting More Information

Inclusions in Koenig's Learning Stack may vary as per policies of OEMs

Request More Information

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Target Audience for Agentic AI with LLMs

Agentic AI with LLMs is a cutting-edge course designed to equip professionals with the skills to work with large language models in AI applications.


  • AI/ML Engineers
  • Data Scientists
  • Software Developers
  • Business Analysts
  • Product Managers
  • IT Professionals
  • Researchers in AI
  • Technical Project Managers
  • Digital Transformation Specialists
  • CEOs and Technology Executives
  • Educators and Trainers in IT
  • Marketing Professionals focusing on AI applications
  • Solutions Architects
  • AI Ethics Consultants
  • Startup Founders in tech-driven businesses


Learning Objectives - What you will Learn in this Agentic AI with LLMs?

Course Introduction

The Agentic AI with LLMs course equips students with essential knowledge and skills to harness large language models for developing intelligent agents, enabling them to understand and implement cutting-edge AI applications.

Learning Objectives and Outcomes

  • Understand the foundational concepts of agentic AI and large language models (LLMs).
  • Learn how to design and build intelligent agents using LLM technologies.
  • Explore the ethical considerations and societal impacts of AI deployment.
  • Gain proficiency in natural language processing techniques relevant to LLMs.
  • Develop skills in fine-tuning and optimizing LLMs for specific applications.
  • Analyze case studies of successful AI-driven projects and implementations.
  • Implement AI solutions in real-world scenarios using practical LLM techniques.
  • Evaluate the performance of AI models and make data-driven improvements.
  • Collaborate effectively in teams to solve complex AI challenges.
  • Stay updated on the latest trends and advancements in AI and LLM research.

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