Course Overview

Agentic AI and Prompt Engineering – Design, Development, and Implementation is a comprehensive 5-day hands-on training program designed to equip participants with the knowledge and practical skills required to design, develop, and deploy modern AI-powered solutions using Large Language Models (LLMs), advanced prompt engineering techniques, and Agentic AI architectures.

Participants will begin by exploring the foundations of Generative AI, understanding how LLMs work, and mastering prompt engineering methodologies that improve AI accuracy, reliability, and performance. The course then progresses into advanced prompting strategies, structured output generation, prompt optimization, and evaluation techniques used in enterprise environments.

Building on these fundamentals, learners will be introduced to Agentic AI—autonomous AI systems capable of reasoning, planning, memory management, tool usage, and decision-making. Through hands-on labs and real-world scenarios, participants will design intelligent agents, multi-agent systems, Retrieval-Augmented Generation (RAG) solutions, and AI workflows using leading frameworks such as LangChain, LangGraph, CrewAI, AutoGen, and Semantic Kernel.

The final modules focus on enterprise implementation, covering governance, security, monitoring, observability, performance evaluation, and industry-specific use cases. By the end of the course, participants will be able to architect, build, evaluate, and deploy scalable Agentic AI solutions that integrate with business systems and support real-world automation initiatives.

Duration: 5 days / 40 hours

Delivery Method: Classroom-based, Virtual Instructor Led Training

COURSE OBJECTIVES

Upon completion of this course, participants will be able to:

·      Understand the evolution of Artificial Intelligence, Machine Learning, Deep Learning, Generative AI, and Agentic AI.

·      Explain the architecture and capabilities of Large Language Models (LLMs) and foundation models.

·      Understand key LLM concepts including tokenization, context windows, temperature, embeddings, inference, and hallucinations.

·      Apply prompt engineering principles to improve AI-generated outputs across various business and technical use cases.

·      Design effective prompts using zero-shot, one-shot, few-shot, role-based, and system prompting techniques.

·      Develop reusable and scalable prompt templates and prompt-chaining workflows.

·      Implement advanced reasoning strategies such as Chain of Thought, Tree of Thought, Self-Consistency, Reflection, and ReAct frameworks.

·      Generate structured outputs including JSON, XML, CSV, and API-ready responses through prompt design and function calling.

·      Evaluate, benchmark, test, and optimize prompts for accuracy, consistency, and reliability.

·      Understand the principles and architecture of Agentic AI systems and autonomous decision-making.

·      Design intelligent agents with planning, memory, tool integration, action execution, and feedback mechanisms.

·      Implement various agent design patterns including ReAct, Planning, Reflection, Tool-Using, and Collaborative Agents.

·      Build and manage multi-agent systems capable of task delegation, coordination, and communication.

·      Utilize modern Agentic AI frameworks such as LangChain, LangGraph, CrewAI, AutoGen, and Semantic Kernel.

·      Integrate agents with external APIs, databases, search engines, and enterprise applications.

·      Implement memory strategies using short-term memory, long-term memory, vector databases, and context management techniques.

·      Design and develop Retrieval-Augmented Generation (RAG) solutions to improve factual accuracy and knowledge retrieval.

·      Apply Responsible AI, security, governance, compliance, and risk mitigation practices when deploying AI agents.

·      Monitor, evaluate, and measure agent performance using observability, tracing, logging, and operational KPIs.

·      Design enterprise-grade Agentic AI solutions for customer service, IT operations, procurement, supply chain, human resources, and finance use cases.

·      Develop an end-to-end Agentic AI implementation strategy that aligns with organizational objectives and business requirements.

PRE-REQUISITES

·      Basic programming knowledge

·      Understanding of APIs and web applications 

·      Familiarity with AI concepts (recommended but not mandatory)

AUDIENCE

·      AI Engineers

·      Solution Architects

·      Developers

·      Business Analysts

·      Technical Leads

·      Automation Engineers