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:
- 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.
-
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. -
Context > clever prompts
An average agent with deep access to your docs, data, and workflows will outperform a “smart” agent working blind. Context compounds. -
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?
A job board focusing on remote impact jobs
This matches what I’ve seen too. Agents start breaking down when we treat them like replacements instead of amplifiers. The biggest wins for me have been around prep work — summarizing inputs, spotting patterns, or keeping things tidy — not making decisions.
The “junior specialist” framing is spot on. Once the scope is narrow and the agent lives close to real context, it actually reduces mental load instead of adding supervision. Curious to see how many teams rediscover this the hard way.
Point 3 hits home. I've been building with multiple LLMs (Claude, ChatGPT, Cursor Composer, Gemini) for over 9 months, and the biggest lesson was exactly this — context beats clever prompts every time.
The task I've offloaded: letting LLMs debate each other. When I'm stuck on a design decision, I ask different models the same question, then share their answers across them. They challenge each other's assumptions. I just make the final call.
The "narrow beats broad" principle resonates. I'm building a tech news aggregator with AI summaries, and the biggest improvement came when I stopped asking the model to "summarize this article" and started asking it to "extract the key technical decisions and their tradeoffs."
Same pattern: tight scope + rich context = reliable output.
One thing I've successfully offloaded: classifying article types (tutorial vs news vs opinion) and routing them to different summary formats. It's not glamorous, but it runs without babysitting and meaningfully improves the output.
Curious — when you mention "agents that persist," are you seeing practical value from memory across sessions, or is it more about continuous operation within a workflow?