Agentic AI in Action: A Beginner’s Guide to Adaptive AI Systems
Duration
4 Hours
Level
Beginner to intermediate Level
Design and Tailor this course
As per your team needs
This course introduces participants to Agentic AI, an advanced form of artificial intelligence capable of autonomous decision-making, goal-setting, and adaptability. Participants will explore how Agentic AI differs from traditional AI/ML and generative AI, focusing on its unique features such as memory, long-term autonomy, and self-directed learning.
Through real-world case studies, and thought experiments, learners will gain practical insights into how Agentic AI is revolutionizing industries like healthcare, finance, and logistics. The course also covers the ethical and regulatory implications, ensuring a responsible approach to developing and implementing autonomous AI systems.
By the end of this course, participants will:
- Understand the evolution of AI and differentiate Agentic AI from other AI paradigms.
- Explore real-world applications where Agentic AI is enhancing productivity and innovation.
- Learn about the architecture of Agentic AI, including memory, goal-setting, and autonomy.
- Examine challenges, ethical considerations, and governance frameworks for responsible AI deployment.
Engage in hands-on exercises, thought experiments, and case studies for practical application.
- Technology Professionals – Developers, AI engineers, and data scientists.
- Business Leaders & Executives – Those exploring AI-driven innovation for competitive advantage.
- AI Enthusiasts & Students – Individuals with foundational AI knowledge interested in autonomous systems.
- Policymakers & Ethicists – Professionals concerned with AI governance, ethics, and regulations.
- Entrepreneurs & Startups – Founders looking to integrate Agentic AI into products/services.
-
- Traditional Work Systems: Manual and rules-based approaches
- AI/ML Systems: Predictive models and automated decision-making
- Generative AI: Content creation and insights using models like GPT
- Agentic AI: Autonomous goal-oriented systems with memory and adaptability
- The transition from reactive to proactive AI systems
- Thought Experiment: What if AI managed an entire business process end-to-end?
- Definition of Agentic AI
- How Agentic AI differs from task-based and generative AI
- Key components: Memory, goal-setting, adaptability, and autonomy
- Real-world examples of Agentic AI in action
- Case Study: Examining how Agentic AI is used in autonomous customer service agents
- Hands-on Activity: Designing an Agentic AI model with basic objectives and decision-making logic
-
- Comparison of AI capabilities
- Decision-making processes
- Levels of autonomy
- Practical use cases
- The shift from automation to autonomy
- Scenario-Based Discussion: Would an Agentic AI system be better suited for your industry?
-
- Core components
- Memory: Short-term and long-term learning in AI
- Goal-Setting: How AI determines objectives and adjusts dynamically
- Autonomy: Self-directed decision-making and adaptability
- How Agentic AI systems are designed and implemented
- Hands-on Demo: Participants analyze a simplified Agentic AI model in action
- Group Challenge: Brainstorm how Agentic AI could optimize a real-world process (e.g., logistics automation)
- Practical Use Cases:
-
-
- Healthcare: AI-driven diagnostics and personalized medicine
- Finance: Automated trading, fraud detection, and risk assessment
- Logistics: Supply chain optimization and autonomous delivery systems
- Advantages of Agentic AI over traditional and generative AI
- Challenges in implementing Agentic AI
- Case Study: How a major company leveraged Agentic AI for supply chain automation
- Workshop: Participants propose how Agentic AI can solve challenges in their industries
-
-
- AI Bias & Fairness: Ensuring unbiased decision-making
- Transparency & Explainability: Making AI decisions understandable
- Regulatory frameworks: Overview of global AI regulations (EU AI Act, US AI Bill of Rights, etc.)
- Ethical dilemmas: Balancing automation with human control
- Case Study: Examining an AI system that failed ethically – what went wrong?
- Group Discussion: Should Agentic AI be allowed full autonomy in high-stakes industries?
-
- Emerging research areas in Agentic AI
- Anticipated advancements and their implications
- What the future of work looks like with Agentic AI
- Prediction Exercise: Learners speculate on AI’s role in the next 10 years
- Final Project: Participants outline an Agentic AI use case for their industry
- Basic knowledge of AI is required; this course is designed for beginners.
