The way humans and technology interact is now making a significant leap forward into a more autonomous future. Just imagine you are working, and just before you get burned out, your next overseas trip is in place, or your ageing parents are given the care they deserve while you are busy with your grind. Yes, all of this is possible now with “agentic AI.” From AI-powered, human-like bots taking care of your parents to supply chain specialists who can optimise the inventory in response to fluctuations in real-time demand. While rules and restrictions bound the previously made AI models, agentic AI will be empowered to act more on your behalf.
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Nothing Just “Proactive”
The promising future and enhanced capabilities of Agentic AI bring us to the burning question: “What is Agentic AI?” To put it simply, we will say agentic AI is nothing but proactiveness.
“It refers to AI systems and models that can act autonomously to achieve goals without the need for constant human guidance. The agentic AI system understands the user’s goal or vision and the context to the problem they are trying to solve.”
Enver Cetin, An AI Expert At The Global Experience Engineering Firm Ciklum
This platform offers a comprehensive set of tools and services designed to help AI agents learn, adapt, and collaborate, enabling them to tackle complex and evolving tasks efficiently. It represents the next frontier of AI, renowned for its capacity to function autonomously—making decisions, adapting to changing circumstances, setting goals, and reasoning independently.
A Sharp Difference
Though agentic AI will harness the creative power of GenAI, they differ in several ways. First, agentic AI is focused on making effective decisions rather than curating engaging content. Second, unlike ChatGPT, Gemini, and other GenAI, agentic AI does not rely on prompts but is fixed on its objectives, such as improving employee engagement, customer satisfaction, and more. Moreover, they are not limited by the rules as their predecessor, GenAI. Agentic AI can carry out complex tasks, autonomously querying databases or initiating workflows to accomplish activities.
Five Steps Towards Automation
Agentic AI employs self-sufficient agents, the AI models that autonomously carry out tasks and manage workflows by leveraging machine learning, algorithms, and predictive analytics to make real-time decisions.
This system operates in a five-step framework to tackle intricate challenges:
Perception
AI agents collect and interpret data from various sources, such as sensors, databases, and user interfaces, transforming raw information into valuable insights. They identify key patterns and extract the most relevant data within their context.
Reasoning
A large language model (LLM) guides the reasoning process by understanding the task at hand, formulating solutions, and coordinating specialised models for functions like content creation or image analysis.
Action
Agents execute tasks by interacting with external systems via APIs. Built-in safeguards ensure security and compliance—such as restricting insurance claim processing to a certain threshold before requiring human intervention.
Learning
Agents improve over time by incorporating feedback from each interaction. This continuous learning process refines their decision-making and optimises performance for greater efficiency.
Collaboration
The open-source nature of these tools fosters a vibrant community of contributors who enhance the platform. This collaborative effort accelerates the development of new features and keeps the technology at the cutting edge of AI advancements.
Fruits of Agentic AI
With the ability to act independently and sound reasoning capabilities, agentic AI is poised to transform the way humans interact with technology, especially in areas like specialisation, innovation and trustworthiness.
Stronger Specialisation
Agentic AI systems are specifically designed to handle highly detailed tasks, allowing for much greater role specialisation compared to earlier, generalised automation systems. Additionally, multiple agentic roles can be developed quickly, making them highly adaptable.
Innovation
Thanks to their advanced judgement and execution capabilities, agentic AI systems are particularly well-suited for experimentation and innovation. For instance, ChemCrow, an AI-driven chemistry agent, has been employed to plan and synthesise a novel insect repellent and create new organic compounds.
Greater Trustworthiness
Agentic AI systems exhibit superior cognitive reasoning, making them less prone to “hallucinations” or fabricated information—a common issue in generative AI models. Furthermore, they excel at evaluating and distinguishing information sources based on quality and reliability, significantly enhancing trust in their outputs and decisions.
With these capabilities and promising rewards coupled with robust security features, Agentic AI is well-positioned to transform how we work. According to Agentforce, by 2028, more than 15% of day-to-day work decisions will be made autonomously through agentic AI, up from 0% in 2024.
