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Agentic Artificial Intelligence (AI) represents a significant evolution in AI, characterized by its ability to autonomously solve complex, multi-step problems using real-time data. As enterprises seek innovative solutions to enhance productivity and drive growth, understanding the role of AI becomes crucial.Agentic AI integrates with large language models (LLMs) to enhance decision-making capabilities using advanced AI models to perform sophisticated reasoning and iterative planning. Agentic AI deploys autonomous agents to interact with enterprise data, execute tasks, and work together without human intervention.
Enterprises can benefit from using Agentic AI in the following ways:
Agentic AI uses span across industries, from customer service to healthcare and manufacturing:
| Banking and financial services | Healthcare |
| Erica by Bank of America is a virtual financial assistant that uses NLP and real-time data to respond to customer queries. This AI agent helps users manage bank transactions, pay bills, track spending, and provide financial advice. | OneRemission is a health-focused AI chatbot that supports cancer patients, survivors, and caregivers. Initially developed by wellness experts and oncologists, it deploys a holistic approach to help users manage cancer-related symptoms and provide post-treatment recovery. |
Key features include:
Despite its many advantages, Agentic AI also comes with challenges and risks, such as:
As businesses navigate the digital landscape, Agentic AI offers a transformative approach to problem-solving and deploying operational efficiency. By leveraging its capabilities, enterprises can focus on harnessing new avenues for innovation and growth.
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AI becomes agentic when it can autonomously assess its environment, set goals, make decisions, and take actions to achieve those goals without human intervention and oversight. Agentic AI is self-driven and demonstrates adaptability with intention.
Agentic AI can integrate with existing enterprise systems, but some limitations exist. Often, custom APIs, secure data pipelines, and compatibility with the current IT infrastructure are required, and integration success depends on the AI’s framework and the enterprise's digital readiness.
By harnessing LLMs and NLPs, AI agents take on diverse forms and distinct capabilities:
Multi-agent systems involve multiple interacting AI agents solving complex tasks. These agents can either cooperate or compete by sharing information, working in parallel, and negotiating. Multi-agent systems are ideal for simulations, autonomous environments, and distributed systems.
Key characteristics of Agentic AI include:
Autonomy: Operating without direct human intervention.
Adaptability: Learning from experiences to improve performance over time.
Goal-oriented: Designed to achieve specific objectives set by users or developers.
Learning-driven: Enhances performance through feedback.
Collaborative: Can work together with other agents and systems.
Agentic AI autonomously sets and achieves goals, adapts behavior based on context, and practices real-time and iterative decision-making. Non-agentic AI performs tasks based on preprogrammed rules and requires human direction to make decisions or alter behaviors.