Based on the provided content, the definitions and differences between Generative AI and Agentic AI are as follows:
Generative AI Generative AI is a subset of artificial intelligence designed to create original content, such as text, images, and code. Unlike traditional AI, which focuses on analyzing existing data and making predictions, generative models use patterns learned from vast datasets to synthesize new, human-like outputs. In a business context, it is used to automate report generation, produce customized investor presentations, and simulate market scenarios. Common architectures include transformer models and Retrieval-Augmented Generation (RAG), which helps improve factual accuracy by grounding outputs in real-time data.
Agentic AI Agentic AI represents an evolution of generative technology by introducing goal-oriented autonomy. While standard generative models simply respond to direct prompts, agentic systems can reason, plan, and interact independently to achieve broader objectives. These systems address the static limitations of generative AI by incorporating memory and tool use to handle complex, multi-step tasks. In corporate advisory, agentic AI can be used for autonomous deal monitoring and orchestrating multi-step due diligence processes, adjusting its strategies in real-time based on past actions and changing market conditions.
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