First, What Is 'Agentic' AI?
Think of the AI you use today—like ChatGPT or Google's Gemini—as a brilliant but passive research librarian. You give it a specific query, and it returns a well-organized answer based on the data it was trained on. It’s incredibly powerful, but it waits
for your next command. Agentic AI is different. It’s more like a project manager. You give it a high-level goal, not just a query. For example, instead of asking, “What are the best marketing strategies for a new coffee shop?” you might tell an AI agent, “Develop a complete digital marketing launch plan for my new coffee shop.” The agent then independently breaks that goal down into smaller steps, conducts research, makes decisions, and executes the tasks needed to achieve the final objective. The key difference is the autonomy—the ability to create and follow a multi-step plan without constant human intervention.
The 'Indian Workflows' Connection
The headline’s mention of India isn’t random; it’s the entire point. For decades, India has been the engine of global business process outsourcing (BPO) and IT services. Complex digital “workflows”—from software development and testing to customer support and data analysis—are handled by vast, highly skilled teams there. This makes it the perfect environment to test and scale agentic AI. These workflows are well-documented, standardized, and digitally native, providing a structured playground for AI agents to learn and operate. A system that can successfully navigate the intricacies of a software development project or a financial reconciliation process in this environment is proving its commercial readiness. It’s a real-world stress test where success means immediate business value, and it’s why this development is being watched so closely.
Meet Devika: An Agent in Action
To make this less abstract, look at a project like Devika. It’s an open-source agentic AI software engineer, positioned as an alternative to the much-hyped American project, Devin. Devika is designed to take a high-level instruction from a user—like “Build a website that displays the current weather”—and handle the entire process. It will plan the necessary features, research the best APIs for weather data, write the front-end and back-end code, debug errors it encounters, and ultimately deploy the application. It’s not just generating code snippets; it's managing the project from concept to completion. While still in early stages, Devika represents the core ambition: to create an AI collaborator that can reason, plan, and build, mimicking the workflow of a human software developer. This is the technology that’s beginning to tackle those complex Indian workflows.
From Goal to Finished Product
So how does an AI agent actually execute a task? The process is a loop of observation, thought, and action. First, it analyzes the user's goal to understand the desired outcome. Then, it creates a strategic plan, breaking the goal into a sequence of logical steps. For each step, it decides which tool to use—it might browse the web for information, access a specific file, write a block of code, or run a test. After taking an action, it observes the result. Did the code work? Did the web search yield the right information? Based on this feedback, it refines its plan, corrects mistakes, and decides on the next action. This iterative cycle of planning, acting, and learning is what allows it to navigate complex, multi-stage tasks that would previously require a team of humans.
The Real-World Implications
The rise of autonomous agents isn't necessarily about replacing human workers overnight, but about fundamentally changing the nature of knowledge work. For businesses, this could mean dramatic gains in efficiency and speed, allowing them to prototype ideas or process data at an unprecedented scale. However, it also introduces new challenges. These systems still require human oversight for quality control, strategic direction, and handling novel problems they aren't equipped for. Instead of managing teams of people performing tasks, managers may soon be directing teams of AI agents, reviewing their work and focusing human talent on creativity and high-level strategy. The conversation is shifting from “Can an AI do this task?” to “Can an AI manage this entire project?”
