This course explains the core principles behind agentic AI, how agent-based systems differ from traditional AI models, and how organizations can apply agentic AI for automation.
Overview
Agentic AI Essentials Training provides a comprehensive foundation in AI systems that can plan, reason, act, and collaborate autonomously. This course explains the core principles behind agentic AI, how agent-based systems differ from traditional AI models, and how organizations can apply agentic AI for automation, decision support, and complex workflows. The training is framework-agnostic while referencing modern agent platforms used in real-world implementations.
Learning Outcomes
- Understand fundamentals of Agentic AI
- Identify components of AI agents and workflows
- Use LLMs with tools, memory, and reasoning
- Design simple autonomous AI agents
- Apply AI agents in business use cases
Duration & Delivery Mode
14 hours
Target Audience
• Business and technology professionals
• AI and automation teams
• Product managers and innovation leaders
• Consultants and solution designers
• Professionals exploring autonomous AI systems
Pre-requisites
• Basic understanding of artificial intelligence concepts
• Familiarity with workflows or problem-solving processes
• No programming or advanced AI background required
Skillset Achieved
• Understanding agentic AI concepts and architectures
• Designing goal-driven AI agents
• Managing agent autonomy, reasoning, and actions
• Applying agentic AI to business and operational use cases
• Evaluating risks and responsible AI deployment
Course Outcome
By the end of this training, participants will be able to understand, design, and evaluate agentic AI systems. Learners will gain the confidence to identify suitable use cases, design agent-driven workflows, and apply agentic AI responsibly across business and professional environments.
Course Outline
Introduction to Agentic AI
• What is agentic AI and why it matters
• Differences between generative AI and agentic systems
• Real-world examples of agentic AI
Core Components of Agentic AI Systems
• Goals, planning, and reasoning
• Memory, tools, and environment interaction
• Feedback loops and iteration
Types of AI Agents
• Reactive, deliberative, and hybrid agents
• Single-agent vs multi-agent systems
• Role-based and collaborative agents
Agent Design Fundamentals
• Defining objectives and constraints
• Managing context and state
• Ensuring reliable agent behavior
Agentic AI Workflows and Automation
• Task decomposition and sequencing
• Decision-making and conditional logic
• Human-in-the-loop agent workflows
Business and Enterprise Applications
• Agentic AI for automation and operations
• Research, analysis, and decision support
• Cross-functional enterprise use cases
Governance, Ethics, and Risk Management
• Bias, accuracy, and hallucination risks
• Data privacy and security considerations
• Responsible and compliant AI adoption
Hands-on Practice and Case Studies
• Agent workflow design exercises
• Real-world agentic AI scenarios
• Participant practice and feedback
Assessment Topics
- Basics of Agentic AI
- Agent architecture and lifecycle
- Prompting and reasoning techniques
- Tool integration and automation
- Real-world agent use cases
Evaluation
• Participation in hands-on exercises
• Agent workflow design assignment
• Knowledge-based assessment
Course Materials
Participants will receive course materials, slides, reference materials, exercises and access to resources for further learning.
Certification
Participants who successfully complete the training and evaluation will receive an AcadNXT Certificate of Completion in Agentic AI Essentials Training, recognizing their foundational expertise in autonomous AI systems.
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What Our Students Say
“Excellent foundational course on agentic AI concepts.”
“Clear explanations with strong real-world relevance.”
“The agent workflow exercises were very insightful.”
“Helped me clearly understand how agentic AI differs from generative AI.”
“A must-attend course for anyone entering the agentic AI space.”