Artificial intelligence has entered a decisive new phase. For most enterprises, the past three years have been defined by Generative AI models that can produce text, code, images, audio, and more. The AI-led momentum accelerated unprecedented productivity gains, redefined content workflows, and brought AI into mainstream business operations. But as adoption matures, organisations are discovering a limitation: generative models, no matter how sophisticated, remain largely reactive. They respond to prompts; they do not act. This gap is exactly where the next frontier, Agentic AI, is accelerating after generative AI.
Analysts say the emergence of Agentic AI is the most important inflexion point since the rise of cloud computing. Agentic AI transforms AI from a passive assistant into an autonomous operator who is capable of planning, decision-making, and multi-step execution.
What is Generative AI?
Generative AI is a class of machine-learning models that create new content in the form of text, images, code, audio, or video by learning patterns from large datasets. In practical terms, it acts as a multi-tasking on-demand creator that can draft emails, write code, generate product descriptions, design visuals and more. Generative AI Output is generated per request, without autonomous continuation.
Systems like GPT, Llama, Gemini, and diffusion models fall under this category.
What Is Agentic AI?
Agentic AI refers to AI systems that can plan, act, and self-manage tasks automatically. Instead of waiting for prompts to act, an agentic AI system takes decisions and executes them on its own, based on a set goal, breaks it into steps, uses tools or APIs, evaluates its own progress, and adjusts its actions as required.
In practical enterprise environments, this means an AI that can automate workflows end-to-end—conduct research, update CRM systems, trigger alerts, execute transactions, or monitor operations without constant human input.
Agentic AI shifts AI from being a content generator to an active operator capable of reliable, goal-driven execution.
Differences: Generative AI vs. Agentic AI
| Focus Areas | Generative AI (GenAI) | Agentic AI |
|---|---|---|
| Human Interaction | High (Prompt Required) | Low (For Goal Setting) |
| Main Work | Content Generation | Plan, act, Make Automatic Decisions, Manage, and Complete Tasks. |
| Autonomy Level | Low | High. |
| Model | Prompt → Output | Goal → Plan → Execute → Evaluate. |
| Dependencies | Human Driven | Tool-driven + Environment Aware. |
| Working Style | Based on Learned Patterns | Adopts in Real Time |
| Examples | GPT, Gemini, Diffusion Models | Research Copilots, Enterprise Decision Making |
| Best For | Ideation, Content Creation, Summarisation | Operational Workflows, Automation, and Continuous Decisions. |
Industry-Wise Uses and Benefits of Generative AI and Agentic AI
Banking, Financial Services, and Insurance (BFSI)
In BFSI, Generative AI helps draft investment reports, summarise regulatory updates, research work and customer communication, and is used for many other tasks.
While Agentic AI in BFSI automates fraud monitoring, executes compliance checks, manages case-resolution workflows, and performs real-time risk assessments by integrating with core banking systems.
Healthcare
In the healthcare industry, Generative AI helps users to manage clinical note summarisation, generate patient educational content, medical coding assistance, and report drafting.
Agentic AI in healthcare is used for automatic patient scheduling, automated claims processing, management of triage workflows, and continuous monitoring on digital health platforms.
Retail and E-commerce
Generative AI in retail and e-commerce helps teams to create personalised marketing content, generate product descriptions, and understand demand patterns and insights.
Agentic AI automates inventory management, dynamic pricing, automatic catalogue updates, and end-to-end customer service workflows.
Manufacturing
Generative AI in manufacturing supports design exploration, documentation, and quality insights.
In manufacturing, Agentic AI enables automatic production scheduling, predictive maintenance, and handles supply chain coordination and management.
Together, they reduce downtime, improve throughput, optimise inventory, and enable faster engineering cycles.
IT and SaaS
Generative AI IT and SaaS support developers and teams in code generation, documentation preparation, and for troubleshooting purposes.
Agentic AI autonomously handles DevOps pipelines, monitors infrastructure, resolves incidents, and more workflows.
Telecom
In the telecom industry, Generative AI enhances customer communication, generates network insights, and supports agent scripting.
While Agentic AI autonomously handles network optimisation, customer queries handling, outage detection, ticket resolution, and service provisioning.
Across industries, the pattern is clear: Generative AI informs and creates, while Agentic AI acts and executes.
Also Read: Global AI Investment to Hit $4 Trillion in Five Years, Says Alibaba’s CEO Eddie Wu
