Google has launched new agentic AI features in India, beginning with restaurant reservations through its AI Mode in Search. This update is more than just another product release. Behind the scenes, it marks a major change in how AI is built and used. We are moving from the chat era into an era focused on action.
In this article, I will explain how this technology works, what it means for the local ecosystem, and how you can approach it as a developer or data scientist.
Google’s Shift to Agentic AI
If you are learning about Machine Learning or Generative AI, you are likely familiar with the usual LLM approach: you provide a prompt, and it predicts the next sequence of words to create a response. This is what we call Generative AI.
Agentic AI works differently. Instead of only generating text, it can carry out multi-step tasks.
You can think of an agent as an LLM that works within a control loop and can use external tools. For example, if someone asks Google, “Find me a table for three, North Indian cuisine, in Hauz Khas for 8 PM tonight,” the system does more than just search a web index. It handles a much more complex process.
- Intent Parsing: It understands the constraints (Party of 3, North Indian, Hauz Khas, 8 PM, Today).
- Tool Calling: It leverages live web browsing capabilities (like Google’s Project Mariner) to query external platforms.
- Real-Time Execution: It checks live availability across multiple booking partners simultaneously.
- Curation & Action: It synthesizes those results into a curated list and generates a direct link to finalize the booking.
As an engineer, I notice that many new developers focus on making their basic LLM smarter. However, real progress comes when you teach a solid model how to use tools like APIs, search indexes, and databases to solve problems on its own. This is what Google is now showing at a large scale.
Why India, and Why Restaurants?
Launching this in India with partners such as Zomato, Swiggy, and EazyDiner is a smart move. India is a large, fast-paced market where people are used to handling everything through connected digital platforms.
Restaurant availability changes constantly. For an agent to work well, the connection between the AI and partner APIs must be very reliable. Google uses its Maps data and Knowledge Graph to connect a user’s natural language request with the strict API requirements of booking platforms.
Google’s agent does not charge your credit card or finish the booking by itself yet. Instead, it gives you a selected list with a direct booking link. From an engineering point of view, this helps reduce risk. When creating autonomous workflows, always add an approval step before letting the agent take actions that cannot be undone.
When should you use this approach in your own projects? If you are creating tools for business workflows, customer support, or operations, move beyond chatbots. Focus on building agents that can access your internal databases, prepare a response or action plan, and let a person review and approve it.
Closing Thoughts
Google’s move into agentic bookings shows that the future will favor those who know how to integrate systems.
If you want to prepare your AI career for the future, here is my advice:
- Learn how APIs work. Your LLM will not be very useful if it cannot connect to other systems.
- Understand state management. An agent must keep track of its progress, know which API calls failed, and figure out how to recover.
I hope you liked this article. Follow me on Instagram for more AI and machine learning tips. You can also check out my book, Hands-On GenAI, LLMs & AI Agents, to get career-ready in AI.
