Agentic AI is making waves in the technology world, capturing the attention of both vendors and enterprises. Unlike the generative AI systems that have garnered so much attention in recent years, these autonomous systems operate with little to no human intervention, working toward a user’s end goal.
With many systems built using a fusion of advanced large language models, natural language processing, reinforcement learning, and machine learning, agentic AI is capable of planning, reasoning, acting, and learning. This enables it to continuously fine-tune and enhance its performance, setting it apart from many conventional AI systems. It can autonomously orchestrate complex tasks and create and manage smaller tasks dynamically to achieve the desired outcome. Advanced agents have contextual understanding and can determine a course of actions and decide what tools (including external databases, APIs, and other AI agents) and assistance are required to reach their desired outcomes.
While generative AI primarily focuses on content creation, including generating text, videos, or audio, and analysing vast datasets to uncover insights, agentic AI goes a step further. It is designed to tackle complex problems, make real-time decisions, and act independently. Businesses are beginning to adopt agentic AI to boost productivity, optimise operations, and drive innovation, making it a game-changer in the AI landscape.
This report discusses how agentic AI is different from traditional AI systems (especially generative AI) and highlights the use cases for which agentic AI is being deployed in support of a range of enterprise functions. The report also lists some examples of leading companies that are already deploying the technology within these functions. Furthermore, the report discusses the key challenges and issues that could hinder the adoption of agentic AI.
