AI Agents Development Training Course

AI Agents Development Training Course

Introduction

AI Agents Development Training Course is designed to equip professionals with advanced skills in building, deploying, and managing autonomous AI systems, intelligent agents, multi-agent architectures, and enterprise-grade AI automation solutions. This comprehensive program explores the foundations of Generative AI, Large Language Models (LLMs), Agentic AI, prompt engineering, AI orchestration, tool integration, reasoning frameworks, memory systems, and autonomous decision-making workflows. Participants learn how modern AI agents can transform business processes through intelligent automation, workflow optimization, conversational AI, digital assistants, and AI-powered enterprise applications.

The course focuses on practical development of next-generation AI agents using leading frameworks, APIs, and cloud platforms. Learners gain hands-on experience designing goal-driven agents, Retrieval-Augmented Generation (RAG) agents, autonomous copilots, enterprise AI assistants, and collaborative multi-agent systems. Through real-world case studies, participants understand how organizations apply AI agents in industries such as healthcare, finance, cybersecurity, customer service, education, and enterprise operations to improve productivity, innovation, and decision intelligence.

Course Duration

5 Days

Course Objectives

1. Understand the architecture and evolution of Agentic AI and autonomous intelligent systems. 
2. Design and develop AI agents powered by Large Language Models (LLMs). 
3. Apply advanced prompt engineering and context engineering techniques for agent behavior optimization. 
4. Build multi-agent collaboration frameworks for complex problem-solving. 
5. Integrate AI agents with APIs, databases, enterprise systems, and external tools. 
6. Develop Retrieval-Augmented Generation (RAG) based AI agents for knowledge-intensive applications. 
7. Implement AI agent memory, reasoning, planning, and decision-making capabilities. 
8. Utilize modern AI agent frameworks including LangChain, LangGraph, AutoGen, CrewAI, and Semantic Kernel. 
9. Create secure and scalable enterprise AI automation solutions. 
10. Evaluate AI agent performance using AI testing, benchmarking, and monitoring methodologies. 
11. Apply Responsible AI, AI governance, security, and risk management practices. 
12. Deploy AI agents using cloud-native AI platforms and MLOps/LLMOps pipelines. 
13. Build production-ready AI copilots and intelligent automation applications. 

Target Audience

1. AI Engineers and Machine Learning Developers 
2. Software Developers and Application Architects 
3. Data Scientists and Data Engineers 
4. Cloud Architects and Solution Designers 
5. Automation and Digital Transformation Professionals 
6. Business Analysts and Innovation Leaders 
7. Enterprise IT Managers and Technology Consultants 
8. Product Managers Developing AI-Powered Solutions 

Course Modules

Module 1: Foundations of AI Agents and Agentic AI

• Understanding AI agents, autonomous systems, and intelligent automation concepts
• Evolution from chatbots to advanced agent-based AI architectures
• Core components of AI agents: reasoning, planning, memory, and actions 
• Introduction to LLMs as the intelligence layer for AI agents
• Case Study: AI customer service agent transforming enterprise support operations 

Module 2: AI Agent Architecture and Design Patterns

• Designing scalable AI agent architectures and workflows
• Understanding perception, reasoning, planning, and execution layers 
• Building goal-oriented and task-driven AI agents 
• Agent communication and orchestration strategies 
• Case Study: Enterprise virtual assistant architecture for employee productivity 

Module 3: Large Language Models for AI Agent Development

• Working with modern LLM technologies and foundation models
• Prompt engineering strategies for agent intelligence 
• Context engineering and instruction optimization 
• Fine-tuning and adapting models for specialized agents 
• Case Study: Healthcare AI agent using LLM-powered clinical knowledge assistance 

Module 4: AI Agent Frameworks and Development Tools

• Building agents using LangChain and LangGraph frameworks
• Developing collaborative agents with AutoGen and CrewAI
• Using Semantic Kernel for enterprise AI applications 
• Integrating APIs, plugins, and external tools 
• Case Study: Multi-agent financial analysis system for investment insights 

Module 5: Memory, Reasoning, and Decision-Making Systems

• Implementing short-term and long-term AI agent memory 
• Developing reasoning chains and planning mechanisms 
• Building adaptive and self-improving agent workflows 
• Managing agent state and knowledge persistence 
• Case Study: AI personal productivity agent managing complex workflows 

Module 6: Retrieval-Augmented Generation (RAG) AI Agents

• Designing knowledge-aware AI agents using RAG architectures 
• Connecting agents with enterprise documents and databases 
• Vector databases, embeddings, and semantic search integration 
• Improving accuracy through retrieval optimization 
• Case Study: Corporate knowledge management AI agent for internal information access 

Module 7: Enterprise AI Agent Security and Governance

• Implementing secure AI agent architectures 
• Managing AI risks, hallucinations, and unauthorized actions 
• Applying Responsible AI and governance frameworks 
• Protecting data privacy and enterprise information 
• Case Study: Cybersecurity AI agent detecting and responding to threats 

Module 8: Deployment, Monitoring, and Future of AI Agents

• Deploying AI agents on cloud and enterprise platforms 
• Building scalable AI agent infrastructure 
• Monitoring performance using LLMOps practices 
• Measuring agent reliability and business impact 
• Case Study: Autonomous business operations agent improving workflow efficiency 

Training Methodology

• Interactive lectures and presentations.
• Group discussions and brainstorming sessions.
• Hands-on exercises using real-world datasets.
• Role-playing and scenario-based simulations.
• Analysis of case studies to bridge theory and practice.
• Peer-to-peer learning and networking.
• Expert-led Q&A sessions.
• Continuous feedback and personalized guidance.

Register as a group from 3 participants for a Discount

Send us an email: info@datastatresearch.org or call +254724527104 

Certification

Upon successful completion of this training, participants will be issued with a globally- recognized certificate.

Tailor-Made Course

 We also offer tailor-made courses based on your needs.

Key Notes

a. The participant must be conversant with English.

b. Upon completion of training the participant will be issued with an Authorized Training Certificate

c. Course duration is flexible and the contents can be modified to fit any number of days.

d. The course fee includes facilitation training materials, 2 coffee breaks, buffet lunch and A Certificate upon successful completion of Training.

e. One-year post-training support Consultation and Coaching provided after the course.

f. Payment should be done at least a week before commence of the training, to DATASTAT CONSULTANCY LTD account, as indicated in the invoice so as to enable us prepare better for you.