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Agentic AI refers to advanced AI systems capable of autonomous action, utilizing complex planning to achieve goals. These systems interact through various levels of AI agents, which can retrieve external knowledge and call APIs to perform tasks effectively. Two implementations, low-code using tools like co-pilot Studio and pro-code using solutions like Semantic Kernel, showcase the creation of these intelligent systems. Emphasizing autonomous functioning, agentic AI can adapt and respond to triggers without constant human input, leading to innovative applications in various contexts. This video illustrates the potential of agentic AI to revolutionize productivity and task management.
Introduction to agentic AI and its significance in today's AI landscape.
Explains retrieval augmented generation and its role in enhancing AI capabilities.
Describes the characteristics of agentic AI, particularly its planning capabilities.
Demonstrates the implementation of AI agents within complex task scenarios.
Comparison of low-code and pro-code solutions for agentic AI development.
The evolution of agentic AI brings forth critical considerations regarding autonomy and ethical implications. As these systems become capable of making independent decisions, ensuring they operate within well-defined ethical frameworks and governance structures is paramount. Given the potential for unintended consequences, organizations must implement robust oversight mechanisms and transparency in AI decision-making processes.
The development of agentic AI signifies a profound shift in human-computer interaction. As these systems gain the ability to autonomously plan and execute tasks, understanding their impacts on user behavior and trust is essential. Research indicates that user engagement with AI systems is significantly influenced by their perceived reliability and transparency, making it critical for developers to prioritize these factors in the design of agentic AI applications.
The video elaborates on its autonomous nature and capabilities in task execution.
It is mentioned as a technique to supplement AI's knowledge beyond pre-trained data.
The video discusses its role in facilitating pro-code solutions for creating agentic AI.
Microsoft is referenced in relation to tools like Semantic Kernel and its collaborative AI development efforts.
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OpenAI's technology forms the backbone of many generative models discussed in the video.
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