Agentic AI represents the next evolution of artificial intelligence, where systems move beyond simple prompts and responses to become autonomous agents capable of planning, reasoning, and executing tasks independently.

This Agentic AI Foundations course introduces the core principles behind AI agents, including decision-making models, memory systems, tool usage, and orchestration frameworks. Participants will gain a strong understanding of how modern AI agents work and how they can be applied to automate workflows, improve productivity, and build intelligent systems.

The course blends conceptual learning with practical demonstrations to help professionals understand how agent-based AI systems are designed and deployed in real-world environments.

Artificial intelligence is rapidly evolving from passive models to active AI agents that can independently perform tasks, collaborate with humans, and interact with digital environments.

This course introduces the architecture and concepts behind Agentic AI, including:

• AI agents and autonomous systems

• Planning and reasoning in AI

• Tool integration and API usage

• Memory and context management

• Multi-agent collaboration

• AI automation workflows

Participants will gain a practical understanding of how organizations are using agent-based systems to automate business processes, enhance decision-making, and create intelligent applications.

Module 1: Introduction to Agentic AI

• Evolution of Artificial Intelligence
• From traditional AI to Agentic AI
• What are AI agents?
• Key characteristics of agent-based systems

Module 2: Core Components of AI Agents

• Perception and input processing
• Reasoning and decision-making
• Action and execution
• Feedback and learning loops

Module 3: AI Agent Architectures

• Single-agent systems
• Multi-agent systems
• Autonomous task execution
• AI orchestration frameworks

Module 4: Memory and Context in AI Agents

• Short-term vs long-term memory
• Context management
• Knowledge retrieval systems
• Vector databases and embeddings

Module 5: Tools and Integrations

• API integrations for AI agents
• Tool usage in agent workflows
• Automating real-world tasks
• Connecting agents with external systems

Module 6: Planning and Reasoning

• Task planning
• Chain-of-thought reasoning
• Decision trees
• Problem-solving workflows

Module 7: Agentic AI Use Cases

• Business automation
• AI copilots
• Intelligent assistants
• Research and data analysis agents

Module 8: Risks, Ethics, and Governance

• Responsible AI
• Security considerations
• AI safety
• Ethical AI deployment