Prompting AI agents feels completely different from prompting chatbots
I’ve been noticing that prompt engineering gets much harder once the AI is expected to actually complete a task instead of just answer a question. With normal chat use, the goal is usually a good response. But with agents, the prompt has to guide behavior across multiple steps, messy websites, changing interfaces, tool errors, missing context, and situations where the agent needs to know when to stop or ask for help. This is what makes products like PineAI/19Pine interesting to me, because the use case is not just “generate a good answer,” it is actually handling real customer support workflows like cancellations, refunds, and billing issues. In that kind of setup, the prompt alone is not enough.
It feels like the real challenge is less about making the model sound smart and more about keeping it stable during execution. Things like state tracking, retries, verification, memory, and clear success conditions seem just as important as the prompt itself.