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What are Gen AI and Agentic AI — and why do they matter?
• Generative AI refers to systems that create new content—text, images, code—based on learned patterns.
• Agentic AI goes a step further: it’s designed to take action, make decisions, plan, and integrate across tools and workflows rather than just respond to prompts.
• From a business perspective, agentic AI enables automation of more complex processes, dynamic decision-making, and integration of multiple AI modules (for example: retrieval, reasoning, memory) rather than just generation.
• For you, working in data science and analytics, this means moving beyond “build a model” to “deploy a system that acts and adapts” — a key differentiator in today’s market.
What you’ll likely learn (and should check in more detail)
• Fundamentals of AI and generative models: how they work, their limitations, how to integrate them.
• Principles of agentic AI: decision-making, planning, autonomy, memory, tool use.
• Building and deploying AI agents: combining LLMs, retrieval, multi-modal inputs, vector databases, orchestrating workflows. (While Fusion’s exact modules aren’t listed, similar programs include this content.)
• Business applications: applying these technologies to real-world use cases in IT services, analytics, automation, data quality assurance, etc.
• Data quality, governance, ethics: because agentic systems operate more autonomously, these aspects become critical.
If you’re looking to stay ahead in the data science/analytics domain, this Gen AI/Agentic AI course looks like a timely opportunity. It provides a bridge from traditional analytics to autonomous AI systems—something many companies will be looking for in the near future. For your role, learning how to design, govern and deploy AI agents could become a differentiator.
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