In previous articles, we introduced Generative AI, Objective-Driven AI, and Interactive AI. Now it seems that the world of artificial intelligence is on the verge of another development: Agentic AI.
Unlike previous models, AI agents can act independently, set goals, and take actions to achieve them. The Agentic AI uses sophisticated thinking processes to autonomously solve complex, multi-step problems, which is essentially a significant transition from reactive Artificial Intelligence, which only responds to stimuli and prompts, to proactive Artificial Intelligence, which can initiate actions and learn from its environment.
How does Agentic AI work?
Agentic AI integrates capabilities of learning, planning, and natural language understanding. Its principles include:
Collecting data from various sources, such as sensors, databases, and digital interfaces.
Processing information, understanding the task, and generating solutions. This stage uses techniques like Retrieval-Augmented Generation (RAG) to access sources, consider different options, evaluate the risks and benefits of each, and provide an accurate and relevant response/course of action.
Execution: By integrating with external tools and software, AI agents can quickly perform tasks based on the plans they've developed. Additionally, "safeguards" can be built for agents to ensure they perform tasks correctly. For example, an AI-based agent in customer service would be authorized to handle claims up to a certain amount, while claims above that amount would need to be approved by a human representative.
Learning: AI agents learn from experience and improve their performance over time. The system can understand and act in a complex environment, identify changes, and act accordingly.
Examples of AI Agent Applications in Various Fields:
Knowledge Management: AI agents can significantly improve how organizations collect, organize, share, and leverage their knowledge by:
Providing recommendations for relevant information, such as articles, research, or products, based on user preferences and behavior in the organization.
Chatbots that provide accurate and complete answers to user questions, using the organization's knowledge ecosystem, managing complex conversations, and delivering precise, personalized responses.
In the Medical Field, AI Agents Could:
Analyze images (such as X-rays, MRI) and other tests to assist in disease diagnosis.
Analyze large volumes of biological data and help in research and development of new drugs.
Additional Examples:
Agents capable of automatically executing transactions in the financial market, using advanced algorithms to identify and manage financial risks.
Agents that can analyze data from machines in industrial factories to predict failures before they occur, perform complex tasks such as assembly, testing, and quality control.
In summary, the field of Artificial Intelligence continues to evolve at an unprecedented pace, with innovative technologies emerging that have the potential to fundamentally transform how we live and work. As research advances and technological capabilities expand, we can anticipate increasingly sophisticated applications of AI, particularly in the realm of agentic AI, where intelligent systems become more adaptive, context-aware, and capable of autonomous decision-making. The ongoing development of these technologies suggests that we are only beginning to explore the profound implications of Artificial Intelligence across various domains.
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