AI agents that actually help (not just demos)
Why most AI agents fail and how to build ones that people actually use every day.
I’ve spent a lot of time looking at AI agent “demos” lately. Most of them are cool for about five minutes, but then you realize they don’t actually move the needle for real work. They’re either too slow, too hallucination-prone, or they require way too much hand-holding.
The Demo Phase Trap
The problem with most agent demos is that they operate in a “perfect world” scenario. The data is clean, the prompt is hand-tuned for that specific 30-second clip, and there’s no real-world noise.
But as soon as you deploy that same agent to a real user, it breaks. Why? Because real users don’t follow scripts. They have messy data, they ask ambiguous questions, and they expect the tool to actually know their context.
Designing for Productivity
When I build agents that are meant to be used every day, I focus on a few simple principles:
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Narrow the Scope Instead of trying to build an “everything app,” I focus on one specific workflow that’s annoying to do manually. If your agent does one thing perfectly, users will love it. If it does ten things poorly, they’ll delete it.
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Fast Feedback Loops If a user has to wait 20 seconds for a response, they’ve already moved on to the next task. Agents need to be snappy. Even if the underlying LLM is slow, your UI shouldn’t be. Use streaming, optimistic updates, and clear progress indicators.
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Trust but Verify AI isn’t perfect. Don’t hide the “thinking” process. Give the user a clear way to see what the agent is doing and why. If it makes a mistake, make it easy for the human to correct it and move on.
It’s Not a Bot; It’s a Tool
At the end of the day, it’s not about making a “smart” bot; it’s about making a tool that feels like a natural extension of how you already work. The best AI agents are the ones where you forget you’re even talking to an AI. They just get the job done.