Agentic AI – Easing AI adoption for the Enterprises

Artificial Intelligence (AI) has moved beyond being a futuristic buzzword—it’s now a business necessity. Over the last 18 months, generative AI has dominated conversations across boardrooms and industries, thanks to its ability to produce human-like responses, create content, and automate workflows. But as we enter the next phase of enterprise AI, a new paradigm is taking shape: Agentic AI.

Unlike generative AI, which focuses primarily on reasoning and creativity, Agentic AI brings an additional—and crucial—dimension: the power to act. It represents a significant leap forward, enabling enterprises to deploy AI agents that not only process information but also observe environments, make decisions, and execute tasks autonomously.

As Narin Kachu, Head of AI Services at Google Cloud India, explains, the rise of Agentic AI is comparable to historical breakthroughs such as the Industrial Revolution or the advent of electricity. It marks a “before and after” moment in the way organizations will operate.

Where generative AI could draft emails, summarize documents, or respond to queries, Agentic AI can close the loop—connecting systems, pulling data from APIs, validating information in real time, and taking meaningful actions within enterprise workflows.

This evolution isn’t incremental; it’s transformational.

The distinction is clear:

Generative AI → Thinks, reasons, and produces human-like responses.
Agentic AI → Goes further by observing the environment, making decisions, and acting on behalf of the user or organization.

For example:

A generative AI tool could summarize a customer support call.
An agentic AI system could analyze the call, score the agent’s performance, recommend training interventions, and even trigger automated workflows to improve service delivery.

This action-oriented capability is what makes Agentic AI a game-changer for enterprises.

Agentic AI is already being deployed across industries, with powerful use cases emerging:

Contact Centers – Instead of analyzing just 5–7% of customer calls (the current industry average), AI agents can review 100% of interactions in real time, flag missed upsell opportunities, and recommend coaching for agents.
Financial Services – AI can assess a shop owner’s creditworthiness by scanning inventory through video input, fetching market prices via Google Search integration, and producing a fair-value assessment in seconds.
E-commerce – Catalog enhancement agents are helping small sellers generate high-quality product images and descriptions, eliminating costly photoshoots while elevating buyer experience.

Employee Productivity – Perhaps the most disruptive impact lies within organizations themselves. By integrating across siloed tools like SharePoint, Google Drive, and analytics platforms, agentic AI dramatically reduces inefficiencies and boosts employee output.

At its core, an agentic AI architecture includes three layers:

The Model – State-of-the-art multimodal large language models (like Google’s Gemini 2.0 with a 1M token context window).
The Tools – APIs, data stores, Google Search, and third-party datasets that feed the agent.
The Orchestration Layer – Where agents collaborate, exchange information, and take action on complex workflows.

This orchestration is critical. It allows multiple AI agents—such as a supervisor agent, a training agent, and a customer support agent—to interact seamlessly, automating entire processes that previously required human coordination.

One of the most exciting examples of this vision in action is Google’s NotebookLM. Originally developed as a research tool, NotebookLM allows users to upload documents, reports, or datasets and then interact with them conversationally.

What sets it apart is its adaptability:

Visual learners can generate structured notes.
Q&A-driven learners can probe documents directly.
Modern learners can even listen to an auto-generated AI-powered podcast summarizing and discussing the uploaded content.

In its enterprise version, NotebookLM takes things further—allowing employees to intercept the AI-generated podcast, ask new questions, and receive voice-based answers in real time. This transforms how organizations conduct research, assess risk, and accelerate decision-making.

We are entering an era where AI agents will act as co-pilots for employees, supervisors for workflows, and creative partners for marketing teams. Their ability to work across contexts, fetch relevant information, and act autonomously will reshape industries—from BFSI to e-commerce to healthcare.

Agentic AI is not about replacing humans it’s about augmenting them, amplifying productivity, and unlocking efficiencies previously thought impossible. For enterprises, the key lies in embracing this shift early, experimenting with agent-driven workflows, and reimagining business models that can leverage the full power of action-oriented AI.