Most AI agent enquiries I get from Bengaluru come from teams who already tried a prompt-only prototype and found it did not survive contact with real users. That is a useful starting point, because the conversation skips straight to the parts that actually matter — tool access, permissions, what happens when the model is wrong, and who approves an irreversible action. Being in the same city helps here specifically, because agent scope is easier to bound in a room with the people who own the systems it will touch.
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Read the full breakdownOften yes, and it is a common starting point in Bengaluru. The prototype usually proves the idea works and needs replacing at the integration layer — permissions, logging, error handling and human approval steps. I would rather audit what you have first than assume it needs rebuilding.
In Bengaluru, yes. Agent projects touch systems owned by different people, and a few days on-site during integration usually saves more time than it costs.
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.
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