AI Agents & Automation in Delhi NCR

Modern models handle transliterated Hinglish reasonably well, but it has to be tested against your actual message history rather than assumed. The failures are usually not comprehension — they are confidence, where the agent answers a message it only partly understood. Making it defer when unsure matters more than making it clever.

It can prepare them, check eligibility and draft the response. I would keep the final approval with a person for anything moving money, at least until you have a body of evidence about where it gets things wrong. That gate costs very little and prevents the category of mistake that is expensive to undo.

A chatbot answers. An agent acts — it calls tools, queries systems, and completes multi-step tasks with defined boundaries. If the value is 'it tells the user something', you want a chatbot. If it is 'it does something on the user's behalf', you want an agent.

It depends on the task, and the honest answer is that you should be able to switch. Route simple classification to a small fast model and reserve a frontier model for reasoning-heavy steps. Building against an abstraction rather than one vendor's SDK keeps that option open.