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Agentic AI: Good Upfront Design Pays You Back Later

Jason Agostoni 2026年07月08日 07:58 1 次阅读 来源:Dev.to

I spend a lot of time preaching architecture and constraints, so it is always nice when a side project gives me receipts. Adding this new feature to DumbQuestion.ai was a good reminder that a well-structured first version lets you spend your next iteration on value, not repair. Below, you will find a few relatively simple challenges and how thoughtful, upfront design made the changes effortless. To vibe or not to vibe ... Many developers jump right in and just rip out an app, ship fast, let the coding agent sort it out, come back and deal with it later. To be fair, that absolutely can get you to first release faster. But even on a solo project, a little proper SDLC discipline pays back later when you want to extend the product without turning every feature into a rescue mission, which is a theme that already runs through how I have been building DumbQuestion.ai. Extend this to the enterprise and you turn a little upfront effort into potential huge savings on token spend Roasting starup pitches (for sport) ... The core idea for Startup Roast was simple enough: take a startup pitch, roast it, and add a reality-check section so the output is not just mockery for mockery’s sake. To illustrate (and avoid just vaguely describing the feature) I picked a random but highly upvoted pitch from Product Hunt: Vida . Vida, which pitches itself as an “AI clone” that learns how you work, remembers what matters, and becomes a “second you,” with early use cases like Reply Rescue, Prompt Rescue, Resume Rescue, Workspace Cleanup, and Daily Wrap. This is a pretty common target use case of agentic AI making it a solid candidate. If you want to skip ahead, here's an example roast for Vida. Combining a preliminary web "market search" into the content yielded a result that was not just sarcastic, but informed. The roast hit the obvious AI-clone positioning, questioned whether the product was really a clone versus a macro suite, and then turned the market context into a sharper Reality Check

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