I’ve been exploring the world of Agents lately, and a question keeps nagging me: when do I really need an Agent instead of just using a ChatGPT-like interface?
In most cases, ChatGPT is enough. You ask a question, get an answer, and you’re done. But there are scenarios where an Agent makes more sense. Here’s my current thinking:
Low-Frequency, One-Off Needs
Many user needs are low-frequency, one-off, and low-risk. For those, a ChatGPT window is usually sufficient. You get your answer, copy some code or text, and you’re done. No Agent required.
When Agents Make Sense
Agents start to make sense when certain conditions are met:
- High-frequency or high-value tasks: Worth automating to save time or resources.
- Horizontal complexity: Need to pull in information from multiple external sources or tools.
- Vertical complexity: Decisions or actions today depend on context or state from previous interactions.
- Feedback loops: The system needs to check results and retry or adjust automatically.
If you don’t have multi-step reasoning, tool orchestration, memory, and feedback, an Agent is often just a chatbot with extra overhead.
The Value of Agents
I feel like many Agent products haven’t thought through what incremental value they add compared to a plain ChatGPT dialog. It’s crucial to have a clear checklist for deciding when an Agent is worth building.
So, what’s your take on this? Do you agree that most low-frequency needs are fine with just ChatGPT? What’s your personal checklist for deciding when an Agent is actually worth building? Share your thoughts and concrete examples from your work where Agents clearly beat a plain chatbot.
*Further reading: What are AI Agents?*