AI agents need better user experience, not just better models
AI agent products need more than stronger models, better prompts, and more connected tools. Users need to know what the agent can do, what happens when it is wrong or stuck, and how to understand long waits, empty screens, or unclear answers.
Helpful design choices include showing the agent’s abilities upfront, avoiding a vague open chat as the only entry point, letting the agent capture confusion or friction during use, reviewing repeated complaints as user experience patterns, and building around specific tasks instead of “ask anything.” A useful agent flow can start with a clear goal, let the agent work, and then use separate verification to check whether the goal was reached. Dynamic screen elements can help show results, with D3 JS mentioned as one option, but the harder design question is deciding what to show and when.
Key points
- Show users what the AI agent can do before they start typing.
- Design around specific tasks instead of a broad open chat box.
- Plan what happens when the agent is wrong, stuck, slow, or unclear.
- Group repeated complaints into user experience patterns, not just separate bugs.
- Use verification outside the agent’s work loop to check whether the goal was actually completed.
Quick term guide
- AI agent
- An AI program that can inspect information and suggest what to do next.
- user experience
- How easy and pleasant it is for a person to use a product.
- agent flow
- A planned set of steps where AI handles several tasks in order.
- verification
- A separate check that confirms whether the agent finished the task correctly.
- AI agents
- AI agents are AI tools that can carry out steps toward a goal, not just answer once.
- model calls
- Requests sent to an AI model to get an answer or action.
- model call
- One request sent to an AI model to get an answer.
- benchmark
- A test used to compare speed, quality, or cost.