Hari Thapliyaal

Dr. Hari Thapliyal is a seasoned professional and prolific blogger with a multifaceted background that spans the realms of Data Science, Project Management, and Advait-Vedanta Philosophy. Holding a Doctorate in AI/NLP from SSBM (Geneva, Switzerland), Hari has earned Master’s degrees in Computers, Business Management, Data Science, and Economics, reflecting his dedication to continuous learning and a diverse skill set.

With over three decades of experience in management and leadership, Hari has proven expertise in training, consulting, and coaching within the technology sector. His extensive 16+ years in all phases of software product development are complemented by a decade-long focus on course design, training, coaching, and consulting in Project Management.

In the dynamic field of Data Science, Hari stands out with more than three years of hands-on experience in software development, training course development, training, and mentoring professionals. His areas of specialization include Data Science, AI, Computer Vision, NLP, complex machine learning algorithms, statistical modeling, pattern identification, and extraction of valuable insights.

Hari’s professional journey showcases his diverse experience in planning and executing multiple types of projects. He excels in driving stakeholders to identify and resolve business problems, consistently delivering excellent results. Beyond the professional sphere, Hari finds solace in long meditation, often seeking secluded places or immersing himself in the embrace of nature.

4 minute read

Developing Solutions with Agentic AI

Course Title: Developing Solutions with Agentic AI

Course Outline

Module 1: Introduction to Agentic AI

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

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

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

Duration

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

Expected Outcomes

  • 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

General Prerequisites

  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)

  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

  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

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

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

2. Development Frameworks and Libraries

Essential for coding and experimenting with agentic AI.

Stable-Baselines3

  • 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

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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