Most people can prompt an LLM. Building one into a product that survives real traffic is a different game entirely. If y

ਜ਼ਿਆਦਾਤਰ ਲੋਕ ਇੱਕ LLM ਨੂੰ ਪ੍ਰੋਂਪਟ (prompt) ਕਰ ਸਕਦੇ ਹਨ। ਪਰ ਇਸਨੂੰ ਅਜਿਹੇ ਉਤਪਾਦ ਵਿੱਚ ਬਦਲਣਾ ਜੋ ਅਸਲ ਟ੍ਰੈਫਿਕ ਨੂੰ ਸੰਭਾਲ ਸਕੇ, ਇੱਕ ਬਿਲਕੁਲ ਵੱਖਰੀ ਚੁਣੌਤੀ ਹੈ। ਜੇਕਰ ਤੁਸੀਂ ਚੈਟ ਵਿੰਡੋ ਵਿੱਚ ਟਾਈਪ ਕਰਨ ਤੋਂ ਅੱਗੇ ਵਧ ਕੇ ਪ੍ਰੋਡਕਸ਼ਨ AI (production AI) ਤਿਆਰ ਕਰਨਾ

RAG lets a model read. Agents let it act.

An agent is fundamentally an LLM stuck inside a loop. It observes, reasons, acts, and then observes again. If you ask an agent to book a flight, it does not just describe how booking works. It breaks the task into steps, calls the right functions, reads the responses, and adjusts.

The loop looks like this. Observe: the agent reads the current state—your request, the results of previous tool calls, any errors. Reason: the LLM decides what to do next, often by generating a structured plan or selecting from predefined options. Act: it calls a tool.

Tools are how agents touch the real world. They are defined with JSON schemas that tell the model exactly what parameters an API needs. The LLM does not make arbitrary HTTP requests. It fills in a schema. "Call the weather API with city: London and units: metric." If the tool returns a temperature, the agent feeds