Agentic Programming Course Outline

Special Note to New Hampshire Residents
This course has not yet been approved by the New Hampshire Department of Education. Please contact us for an update on when the class will be available in New Hampshire.

Overview
This course delves into designing AI systems around autonomous agents capable of making decisions, learning from their environment, and interacting with other agents. You'll learn how to program these agents to act independently using agent-oriented principles, moving beyond traditional programming paradigms.

The class will focus on creating intelligent AI agents, exploring their applications, and understanding the underlying theories that guide their behaviors in dynamic environments.

Note: As AI technology evolves, the course content will be updated periodically to reflect new trends and advancements.

Prerequisites
Basic knowledge of AI and programming concepts is recommended.

Course Outline

BACKGROUND

Introduction to AI Agents and Agentic AI
What are AI agents, and how do they differ from traditional software?
The concept of agency in AI: autonomy, adaptability, and interactivity
Overview of agentic AI's role in transforming technology and business

Principles of Agent-Oriented Programming (AOP)
Core elements of AI agents: beliefs, desires, and intentions (BDI)
How AOP directly applies to AI agent behavior modeling
Understanding the agent's environment and its impact on decision-making

DESIGNING AND PROGRAMMING AI AGENTS

Building Intelligent Agents
Steps to design autonomous AI agents with adaptive capabilities
Programming agents to perceive, reason, and act in uncertain conditions
Introduction to reinforcement learning techniques for agent training

Developing Multi-Agent Systems
Creating collaborative AI systems where multiple agents interact
Techniques for communication protocols and coordination among agents
Handling conflicts and cooperation in multi-agent environments

REAL-WORLD APPLICATIONS OF AI AGENTS

AI Agents in Business and Industry
Real-world use cases in robotics, virtual assistants, and smart automation
Implementing AI agents in finance, logistics, and customer support
Evaluating the impact of agentic AI on decision-making processes

Ethical Considerations in AI Agent Development
Addressing ethical challenges when designing autonomous agents
Exploring transparency, accountability, and biases in AI decisions
Future trends and the role of responsible AI development

HANDS-ON PROJECTS

Building a Functional AI Agent
Step-by-step guide to creating an AI agent using AOP principles
Implementing adaptive decision-making processes in agent design
Testing and refining the agent’s interactions within a simulated environment

Advanced AI Agent Design Challenges
Expanding single-agent capabilities to handle complex tasks
Case studies on scaling AI agents for enterprise-level applications
Best practices for improving agent performance and reliability

View outline in Word

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