Course Title: Developing Solutions with Agentic AI

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Course Outline

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Module 1: Introduction to Agentic AI

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• 1.1 Understanding Agentic AI

  • Definition and key concepts.
  • Difference between agentic and traditional AI systems.
  • Real-world examples of agentic AI.

• 1.2 The Rise of Agentic AI

  • Evolution from reactive to proactive AI systems.
  • Trends and advancements in AI autonomy.

• 1.3 Importance of Agentic AI

  • Benefits of agentic AI in various industries.
  • Ethical considerations and potential risks.

Module 2: Foundations of AI Agents

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• 2.1 Core Components of AI Agents

  • Perception: Sensing and understanding the environment.
  • Decision-making: Planning and executing tasks.
  • Action: Interacting with the environment.

• 2.2 Agentic Architectures

  • Reactive agents.
  • Goal-oriented agents.
  • Utility-based agents.
  • Learning agents.

• 2.3 Multi-Agent Systems

  • Collaboration and competition between agents.
  • Swarm intelligence and distributed decision-making.

Module 3: Building Blocks of Agentic AI

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• 3.1 AI Technologies Enabling Agency

  • Reinforcement learning.
  • Natural language processing (NLP).
  • Computer vision.
  • Generative AI.

• 3.2 Toolkits and Frameworks

  • Introduction to OpenAI’s GPT models and APIs.
  • Using LangChain for multi-agent systems.
  • Integration with robotics, IoT, and APIs.

• 3.3 Hands-On Setup

  • Setting up development environments.
  • Creating simple autonomous agents.

Module 4: Applications of Agentic AI

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• 4.1 Industry Use Cases

  • Customer support chatbots.
  • Autonomous supply chain management.
  • Personal assistants and task automation.

• 4.2 Business Case Analysis

  • ROI from implementing agentic AI.
  • Competitive advantages and scaling AI systems.

• 4.3 Customization and Deployment

  • Tailoring agent behavior to specific business needs.
  • Deployment strategies for production environments.

Module 5: Ethical and Societal Implications

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• 5.1 Responsible AI

  • Bias and fairness in autonomous decision-making.
  • Transparency and accountability in agent behavior.

• 5.2 Regulatory Compliance

  • Current regulations and standards for AI systems.
  • Preparing for future legal challenges.

• 5.3 Mitigating Risks

  • Avoiding over-reliance on autonomous agents.
  • Managing unintended consequences.

Module 6: Advanced Topics in Agentic AI

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• 6.1 AI-Driven Self-Improvement

  • Agents capable of continuous learning.
  • Feedback loops for optimization.

• 6.2 Multi-Agent Collaboration

  • Designing complex systems with multiple interacting agents.
  • Managing conflict and cooperation.

• 6.3 AI and Human Collaboration

  • Augmenting human capabilities with agentic AI.
  • Hybrid systems for decision-making.

Module 7: Capstone Project

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• Participants work in teams to develop and deploy an agentic AI solution tailored to a real-world problem within their industry.

Module 8: Future Trends and Continuous Learning

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• 8.1 Emerging Technologies

  • The intersection of Agentic AI and GenAI.
  • AI with emotional intelligence.

• 8.2 Building a Learning Organization

  • Tools and resources for staying up-to-date with AI advancements.
  • Encouraging experimentation and innovation.

Training Methods

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• Interactive lectures and discussions.
• Hands-on workshops and labs.
• Case studies and group projects.
• Guest speakers from AI-focused industries.

Duration

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• 5 Days (Flexible depending on depth of coverage per module).

Expected Outcomes

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• A strong grasp of agentic AI fundamentals and applications.
• Hands-on experience in developing and deploying AI agents.
• Enhanced understanding of ethical and strategic considerations.
• A roadmap for implementing agentic AI within your organization.

Prerequisites

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General Prerequisites

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1. Basic Understanding of AI Concepts

   • Familiarity with terms like machine learning, deep learning, and artificial intelligence.
   • Awareness of common AI applications.

2. Programming Knowledge

   • Intermediate proficiency in Python (e.g., working with libraries, creating scripts).
   • Familiarity with Jupyter Notebook is a plus.

3. Problem-Solving Skills

   • Ability to break down complex problems into manageable components.
   • Experience working on structured projects or workflows.

Technical Prerequisites (nice to have)

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1. Mathematics and Statistics

   • Fundamental knowledge of linear algebra, probability, and basic calculus.
   • Understanding concepts like optimization and regression.

2. Machine Learning Basics

   • Familiarity with supervised and unsupervised learning.
   • Understanding concepts like neural networks and reinforcement learning (helpful but not mandatory).

3. Software Tools

   • Experience with AI libraries like TensorFlow, PyTorch, or scikit-learn (preferred).
   • Familiarity with Docker or virtual environments for software setup.

Team and Infrastructure Requirements

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1. Team Member Roles

   • At least one participant familiar with software engineering or data science.
   • Others can have domain expertise to provide context for real-world applications.

2. Hardware/Software Setup

   • Laptops with at least 16GB RAM (32GB recommended for training with large datasets).
   • GPU support for deep learning tasks (if possible).
   • Pre-installed software:
     • Python 3.8+.
     • Development environments (e.g., PyCharm, VSCode).
     • Libraries like NumPy, Pandas, Matplotlib, LangChain, and OpenAI API.

Infrastructure / Platform Required

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Choosing the right platforms for an Agentic AI Training course is crucial for hands-on learning and scalability. Here’s a curated list of platforms that can support your training effectively:

1. Cloud-Based Platforms

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These platforms are ideal for scalable computing resources, especially for AI and machine learning tasks requiring GPUs or TPUs.

• For free GPU/TPU: Google Colab
• For deployment
  • AWS SageMaker
  • OR Azure Machine Learning / Azure AI Foundary
  • OR Google Vertex AI / Gemini AI 2.0

2. Development Frameworks and Libraries

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Essential for coding and experimenting with agentic AI.

• LangChain
• OpenAI
• DeepSeek or LLAMA
• Ollama

Stable-Baselines3

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• A library for reinforcement learning with pre-built algorithms. Stable-Baselines3
• Scalable framework for training distributed AI agents. Suitable for advanced multi-agent system projects. Ray/RLlib

4. Visualization and Testing Tools

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To visualize and test agentic AI behaviors in real-time.

• Offers a 3D simulation environment for testing agents in virtual worlds. Unity ML-Agents
• Simplifies the creation of interactive dashboards to visualize agent performance. Streamlit
• Provides real-time monitoring of agent training and performance metrics. TensorBoard

5. Code Hosting and Collaboration

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• GitHub
• Replit