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Agent = Model + Harness.
Different AI workflows don’t require different interfaces.
The separation is straightforward:
🧠 Model → Determines the interaction and supplies context.
⚙️ Harness → Validates requests, manages state and executes actions.
🧩 UI → Renders the appropriate reusable component.
Three workflows. One option picker. Different choices.
The model decides what interaction is needed. The application controls how it behaves.
That’s how you build flexibility without sacrificing predictability.
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When a CRM-to-database sync fails halfway, how do you handle it: idempotent retries, or a reconciliation pass afterward? That's usually where integrations start to drift.
Designed an AI research workspace where the agent plans the work and fills a sheet with cited sources as it goes.
Same screen, two modes. Which one would you work in all day? 👀
13 voted
72%
5 voted
28%
18 votes
Closed
Keeping the research plan and cited results side by side is a useful way to make the agent’s work easier to inspect. I’d lean toward light mode for reading the table in daylight. How do you distinguish a verified source from one that still needs review?
Working on an AI agent that takes the hard work out of adding asset formats. It suggests hooks, you just add 4 to 40 images and see the magic .
Turning complex video creation processes into something as simple as possible.
Dropping in a pile of images and getting hooks suggested back would save so much time. Who's it mainly for, ad agencies or solo creators?