Most people are using AI agents wrong

I keep seeing the same pattern with AI agents: people install one, ask it to “run marketing” or “handle ops,” get underwhelmed, and conclude that agents are overhyped.

The problem isn’t the tech. It’s how we’re using it.

From what I’ve seen (and tested), AI agents only become useful when you treat them like junior specialists, not magic employees.

A few practical principles that actually work:

1. Narrow beats broad, every time
   Agents perform best when scoped tightly:

“Maintain my Google Ads negative keyword list”

“Classify and log expenses weekly”

“Summarise inbound support tickets and flag edge cases”

If your prompt sounds like a job description, it’s already too vague.

2. Give them leverage, not responsibility
   The best agents don’t decide, they prepare.
   They surface options, patterns, drafts, or anomalies so you can act faster with less mental load.

3. Context > clever prompts
   An average agent with deep access to your docs, data, and workflows will outperform a “smart” agent working blind. Context compounds.

4. Agents beat tools when they persist
   The real shift isn’t chatbots, it’s agents that remember state, operate continuously, and improve over time inside your workflow.

That’s why some early platforms (e.g. Motion and Elixa) are focusing less on flashy demos and more on operational fit, agents that live inside real work environments.

My take:
AI agents won’t replace teams overnight. But they will quietly remove 20–40% of the cognitive overhead that burns founders and operators out.

What’s one task you’ve successfully offloaded to an agent without babysitting it?