What is Agentic AI ?

By now, everyone has heard something related to AI, AI agents, GenAI or AI automation. Recently there has been a new paradigm, “Agentic AI” - also interchangeably called AI agents, agentic intelligence, or agentic systems. Let's try to understand how it stands out. Agentic AI can act independently to fulfill defined goals. It differs from generative AI systems in a way that agentic systems can act autonomously by understanding a goal and based on real-world feedback, refine their strategies. Agentic systems have a differentiating advantage of ‘constant memory’  over generative AI.

Here is the summary of the differences between generative AI and Agentic AI,

Tech leaders from Bill Gates to Jensen Huang are optimistic about the transformative potential of AI agents claiming them as the biggest revolution in recent times. However, in reality, they have become a bubble of over-expectations. Some of the challenges of implementing fully autonomous agents include,

Despite the challenges, AI agents are learning and improving fast. One great example is the significant enhancements in text to speech and text to video in 2025 as compared to past year.

Increased autonomy and decision-making capabilities:
Agentic AI is expected to move towards fully autonomous operations, requiring even less direct human intervention. AI agents will become increasingly adept at reasoning, planning their actions, and even self-checking their work, enabling them to handle tasks that currently require specialized human skills. Future models are likely to become more proactive, capable of self-improvement, setting their own objectives, and acting as more independent problem-solvers. Instead of merely executing predefined tasks, they will be able to refine their strategies, learn from past mistakes, and adjust their objectives based on evolving conditions, essentially acting as true digital collaborators . The scale of deployment could also increase dramatically, with some envisioning a future where organizations operate with millions of agentic AI systems working under human supervision.

Enhanced reasoning and problem-solving abilities:
Agentic reasoning, which emulates human-like problem-solving skills, will serve as the core "brain" of AI applications. This will enable AI to better understand user intentions, gather relevant contextual information, strategically plan its actions, execute tasks in a logical sequence, and continuously learn and improve over time. Reasoning models are rapidly becoming essential components of the agentic AI ecosystem, empowering these systems to navigate intricate problems, uncover hidden connections within data, and make logical decisions autonomously in dynamic environments. Agentic AI will increasingly leverage sophisticated planning tools such as planning graphs, decision trees, and pathfinding algorithms to optimize its navigation through complex problems and decision spaces . The very process of agentic reasoning involves distinct stages, including planning the approach, retrieving necessary information, and generating the final output, all working together to process user inputs, dynamically access contextually relevant data, and produce accurate and actionable results .  
 

Rise of multi-agent systems:
These agents will collaborate, communicate, and coordinate their efforts to solve complex problems with greater efficiency than standalone models . The use of multiple agents working together can significantly enhance the quality of the outcomes and the complexity of the work that can be accomplished compared to relying on a single AI agent . Within these multi-agent systems, different agents can be assigned specific and distinct roles to optimize performance and streamline workflows. These systems can be structured in various ways, including centralized networks where a central unit manages information flow and decentralized networks where agents share information with their neighbors . The development of open-source frameworks like AutoGen, CrewAI, and MetaGPT is facilitating the creation and deployment of these multi-agent systems . Furthermore, the ability to quickly construct and customize teams of AI agents based on changing business needs, without requiring extensive coding, will provide organizations with unprecedented agility .  
 

Seamless integration:
The seamless integration of agentic AI with other key technologies like cloud computing, the Internet of Things (IoT), and robotics is another critical trend. AI agents are increasingly leveraging the power of cloud computing for distributed processing and parallel execution, significantly expanding their capabilities. Agentic AI is also being designed to integrate smoothly with existing business systems, such as inventory management platforms, customer databases, and various sales tools, enhancing overall operational efficiency . The advent of cloud computing has already revolutionized the accessibility of AI, allowing systems to scale rapidly and process vast amounts of data in real time . Moving forward, agentic AI will be increasingly integrated with physical technologies like robotics and the Internet of Things, enabling businesses to automate core processes and gain deeper, more actionable insights from their data.

Proactive and personalized applications:
AI assistants are expected to evolve from simply responding to prompts to becoming proactive collaborators, anticipating user needs and offering helpful suggestions in real-time . This will lead to the development of significantly more personalized and responsive applications, fundamentally changing how we interact with and utilize software . The long-term vision for agentic AI extends beyond mere automation to encompass personal adaptation, with AI systems becoming increasingly individualized, learning a user's specific preferences and working style to provide highly customized support.  AI-powered personal assistants will become more adept at understanding and responding to natural language commands, leading to more intuitive and effective interactions . Agentic AI will transform customer support services by enabling software agents to provide personalized, around-the-clock service that goes beyond simple FAQs and automated responses . Even in marketing, AI agents will enhance strategies by analyzing consumer behavior and autonomously personalizing marketing campaigns to maximize their effectiveness