In a world increasingly shaped by automation and intelligence, a new paradigm is emerging—one where AI isn’t just reactive but truly agentic. Imagine systems that don’t just process data but reason, plan, and act independently, adapting dynamically to their environment.
From self-driving enterprises optimizing workflows to AI autonomously negotiating deals, agentic AI is redefining decision-making, problem-solving, and human-AI collaboration. It’s the next step beyond traditional machine learning—a shift from passive algorithms to goal-driven digital agents that can operate with minimal human intervention.
As we stand on the brink of this intelligent revolution, the question isn’t just about what AI can do, but what it should do—and how we shape its evolution responsibly. Are we ready to embrace AI that thinks, acts, and learns on its own terms?
This week, The People Weekly delves into the ever-changing world of agentic, exploring how advancements in AI, IoT, and big data are transforming the way we experience and navigate the world. Stay tuned as we uncover the innovations shaping the future.
What is Agentic AI?
Agentic AI refers to artificial intelligence systems that are capable of autonomous action and decision-making to achieve specific goals with minimal or no direct human intervention. These systems, often called AI agents, can perceive their environment, reason, plan, and execute actions based on their objectives and the feedback they receive.
Unlike traditional AI models that only respond to specific inputs or commands, agentic AI operates more like an intelligent assistant or collaborator, capable of reasoning, planning, and adapting its actions based on changing objectives.
What makes AI “Agentic”?
At its core, what makes AI agentic is its capacity for autonomous action and decision-making to achieve specific goals within an environment, with limited or no direct human intervention. It goes beyond simply processing data or generating content; it involves understanding context, reasoning, planning, and acting.
The key characteristics that contribute to an AI’s “agency” are,
Autonomy
Goal-Orientedness
Perception
Reasoning
Planning
Decision-Making
Action/Execution
Learning and Adaptation
Language Understanding (NLP/NLU)
Memory
Interaction
How does Agentic AI work?
Agentic AI functions through an integrated system of intelligent components that enable it to perceive, plan, decide, and act—much like a digital co-worker. At its core, a network of software components called “agents” collaborates to process vast volumes of data and gradually learn behaviors over time. Each agent is designed with specific goals and capabilities, and they operate collectively to solve complex problems. This forward-thinking approach draws on advanced technologies like machine learning, natural language processing (NLP), and knowledge representation, empowering agents to reason, learn, and communicate with efficiency.
Agentic AI operates through a four-step process:
Perception
An AI agent collects and processes information from multiple sources, such as sensors, databases, and digital platforms, to build a comprehensive understanding of its environment. By integrating real-time and historical data, the agent can detect patterns, identify anomalies, and recognize contextual nuances that influence its decision-making.
Reasoning
Leveraging sophisticated algorithms and large-scale language models, the AI agent interprets the acquired data to derive meaningful insights. It applies logical inference, probabilistic reasoning, and pattern recognition to generate optimal solutions. Additionally, the agent coordinates with specialized models tailored for specific domains, ensuring precise and effective problem-solving across various tasks.
Action
The AI agent translates its insights into tangible outcomes by interfacing with external systems, applications, and software through APIs. It autonomously executes tasks such as data retrieval, content generation, system updates, or decision support, following structured workflows. Its ability to adapt to dynamic conditions ensures that actions align with predefined objectives and real-time requirements.
Learning
A continuous feedback loop enables the AI agent to refine its decision-making processes. By assessing the effectiveness of its actions, the agent updates its models, adjusts parameters, and improves its performance over time. This iterative learning mechanism ensures that the agent becomes increasingly efficient, accurate, and responsive to new challenges and evolving data patterns.
Agentic AI market
According to Forbes, the agentic AI market is projected to grow at a remarkable CAGR of 35%, reaching an estimated $126.9 billion by 2029. Statista reports that in 2024 alone, the market value stood at $5.1 billion. Meanwhile, Capgemini anticipates this figure will exceed $47 billion, driven by a compound annual growth rate of over 44%. This explosive growth underscores the transformative power of agentic AI in enabling autonomous decision-making across industries.Gartner predicts that by 2028, at least 15% of day-to-day work decisions will be made autonomously through agentic AI.
Indian Scenario
India’s agentic AI market is experiencing rapid growth, driven by increasing enterprise adoption and substantial investments. In 2024, the enterprise agentic AI market in India was valued at approximately USD 132.6 million and is projected to reach USD 1.73 billion by 2030, growing at a compound annual growth rate (CAGR) of 53.9% from 2025 onwards.
A Deloitte report states that India is emerging as the global leader in adoption of Agentic AI “with over 80% of businesses actively exploring the development of autonomous agents”. ServiceNow, a cloud-based PaaS MNC, is looking at India as the key market for agentic AI.
Gupshup, the conversational tool, as reported by INC42, recently launched its library of 15 pre-built and customisable AI agents. It is providing its Agentic AI capabilities to startups like Lenskart and Cars24, as well as to companies like Kotak and Tata AIA in the financial sector.
Zomato’s “Nugget”, Zohocorp’s Zia Agents, Infosys building over 100 AI agents,are all examples of how the Indian Agentic AI market is booming.
Government initiatives further bolster this growth. The Indian government has announced a $1.25 billion investment in AI projects, aiming to enhance computing infrastructure and support AI startups. Such efforts are positioning India as a significant player in the global agentic AI landscape.
Relevance Across Industries
The versatility of agentic AI makes it pertinent to numerous sectors:
Human Resources and Recruitment: Agentic AI streamlines the hiring process by automating tasks such as generating job descriptions, identifying suitable candidates, drafting personalized communication, and scheduling interviews. LinkedIn’s AI-powered “hiring assistant” exemplifies this application, enhancing efficiency and allowing recruiters to focus on the interpersonal aspects of hiring.
Finance: Agentic AI is transforming finance by enabling smarter, autonomous decision-making. It powers algorithmic trading, real-time fraud detection, and personalized financial planning. It also streamlines compliance reporting, loan processing, and adapts strategies to individual client needs—boosting speed, accuracy, and personalization.
Manufacturing: In manufacturing, agentic AI optimizes production lines by autonomously monitoring equipment performance, predicting maintenance needs, and adjusting workflows to enhance efficiency and reduce downtime.
Logistics and Supply Chain Management: Agentic AI is streamlining logistics and supply chain management by enabling autonomous route optimization, predictive maintenance, and smart inventory management. It analyzes real-time data to enhance delivery efficiency, forecast equipment issues, and automate stock control—while also adjusting logistics dynamically to prevent bottlenecks and meet shifting demand.
Retail: Retailers employ agentic AI to manage inventory, personalize customer experiences, and optimize pricing strategies by analyzing purchasing patterns and market trends.
Customer Service: Agentic AI enhances customer support by resolving inquiries, processing transactions, and providing personalized recommendations without human intervention. Gartner predicts that by 2029, agentic AI will autonomously resolve 80% of common customer service issues . Recent news about how AI has enhanced customer service by Salesforce Agentforce is a great example.
Healthcare : Agentic AI is reshaping healthcare by delivering intelligent, real-time support. It powers personalized treatment plans, speeds up drug discovery, and enhances surgical precision with real-time guidance. It also monitors patient data from wearables for early alerts and automates admin tasks like scheduling and records—boosting efficiency across the board.
Emerging Industries: Agentic AI is gaining traction in emerging industries by boosting efficiency and intelligence. In construction, it helps optimize schedules, manage resources, and monitor safety. The energy sector benefits from smarter consumption and grid control. In legal and compliance, it automates document processing and reporting, while in transportation, it streamlines logistics and delivery operations.
The Impact of Agentic AI on Jobs
Agentic AI is poised to reshape the workforce—not just by automating tasks, but by changing how work itself is approached. Unlike traditional AI, which automates specific functions, Agentic AI can independently plan, execute, and adapt across multi-step workflows. This means it can take on roles traditionally requiring human judgment, problem-solving, or initiative.
On one hand, this creates concerns around job displacement, especially in areas like customer service, basic data analysis, content moderation, and administrative roles where agents can work faster and more accurately. Tasks that are repetitive, rule-based, or time-consuming are most susceptible to being handed over to AI agents.
However, Agentic AI is also a powerful enabler. It allows professionals to offload routine work and focus on higher-value activities—augmenting rather than replacing human talent. For example, marketers can spend more time on strategy while AI handles campaign execution. Financial analysts can make smarter decisions with AI-powered insights. Developers may collaborate with AI agents that write, test, and debug code.
Importantly, Agentic AI is expected to create new roles that didn’t exist before, such as AI trainers, ethicists, prompt engineers, and multi-agent system architects.
PeopleLogic Opinion
Agentic AI isn’t just another leap in technology—it’s a reimagining of how intelligence can function at scale. By equipping machines with the ability to perceive, reason, and act autonomously, we’re witnessing the rise of digital agents that don’t just assist but actively collaborate and innovate alongside humans.
Its rapid adoption across industries—from healthcare and finance to logistics and HR—signals a fundamental shift in how organizations operate and deliver value. India, in particular, stands at the forefront of this transformation, with robust enterprise interest, government backing, and a growing ecosystem of AI-driven innovation.
Yet, with great power comes greater responsibility. The journey forward must be guided by thoughtful governance, ethical design, and inclusive strategies to ensure Agentic AI amplifies human potential rather than replacing it. As we move into this agent-powered era, the question is no longer whether Agentic AI will change our world—but how we, as individuals, businesses, and societies, will shape its role in it.
