AI is moving fast.
Generative AI can write, code, summarize, and create. But the next wave of AI is going further. These systems don’t just generate content they can plan, reason through problems, use tools, and take actions on their own.
In this masterclass, we’ll talk about what’s really changing. Not just the buzzwords, but how AI systems are actually evolving from simple prompt-based models to structured, agent-driven systems that can handle real tasks.
If you’re thinking about building a career in Machine Learning and AI, this session will help you step back and see the bigger picture. What skills actually matter? What should you focus on? What does “industry-ready” really mean in today’s AI landscape?
The goal of this session is simple: give you clarity. So you can move forward with direction, not confusion.
What Is Generative AI vs Agentic AI?
Generative AI models generate text, images, code, and data based on learned patterns. Agentic AI systems go a step further, they use reasoning, memory, and tools to execute multi-step tasks and automate decisions.
In this session, you will understand:
- How generative AI models like LLMs work
- The limitations of prompt-based systems
- What makes an AI system “agentic”
- Architecture behind autonomous AI agents
- Skills required for AI engineers in 2026
- Real-world use cases of agentic AI in enterprises
This is not a surface-level introduction. It is a structured discussion on how AI systems are built and deployed in real-world environments.
Why This Webinar Matters
The AI industry is shifting from:
- Prompt Engineering --> AI System Architecture
- Content Generation --> Autonomous Execution
- Model Usage --> End-to-End ML Deployment
If you want to stay relevant and competitive in Machine Learning and Artificial Intelligence roles, understanding this shift is critical.
This session will help you see where the industry is moving and how you can move with it.
