Sumit Gupta

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The gap between a "cool AI prototype" and a tool that actually survives the real world is massive. It's the difference between a magic trick and a utility.

When you're building with LLMs, you have to stop thinking like a coder and start thinking like a coach. You wouldn't expect a new intern to know your entire codebase on day one without a handbook. So why do we expect an agent to handle edge cases without clear guardrails?

I've spent the last week refining our internal prompts, and the biggest wins didn't come from "better" models. They came from better instructions. To be really honest, I spent way too long trying to code my way out of a problem that just needed a clearer explanation.

If you're struggling to get your agents to behave, try treating them like a human hire. Give them the tools, give them the context, and then get out of the way. 🚀

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