Design implications are underrated - agentic systems need different UX patterns than chatbots. You're not designing conversations, you're designing supervision interfaces and escalation flows.
My agent's UI is mostly status dashboards and override controls, not chat interfaces. The mental model shift is hard: from "tool I use" to "teammate with specific capabilities." Most agent UX still looks like glorified chatbots because designers haven't internalized what autonomous operation actually means.
I used the evaluator(critic) and executor loop extensively in my projects! In my opinion they are the most powerful orchestration technique since you can define clear checklist of what correct/good looks like and it will loop until the critic checks off all the checkmarks
Loved how you broke down the concept and highlighted the core technical distinction between a standard LLM and an AI Agent. Also, your agents are the cutest!
Thank you, Ileana. This is an excellent post that really clearly explains what agents are and what they're not. I particularly liked how you broke down the ways in which different companies find agents and cut through the fluff to show that they're basically saying the same thing.
The most underestimated risk of agents isn’t autonomy.
It’s the speed of failure.
A bad interface fails locally.
A bad agent scales the mistake.
If limits aren’t designed first, you’re not building intelligence —
you’re accelerating impact.
Design implications are underrated - agentic systems need different UX patterns than chatbots. You're not designing conversations, you're designing supervision interfaces and escalation flows.
My agent's UI is mostly status dashboards and override controls, not chat interfaces. The mental model shift is hard: from "tool I use" to "teammate with specific capabilities." Most agent UX still looks like glorified chatbots because designers haven't internalized what autonomous operation actually means.
True. Not every AI or agentic experience needs to be conversational or have a personality.
Part of designing for AI is understanding what kind of relationship or interaction you're designing for.
I used the evaluator(critic) and executor loop extensively in my projects! In my opinion they are the most powerful orchestration technique since you can define clear checklist of what correct/good looks like and it will loop until the critic checks off all the checkmarks
Thank you for the great example Tyler!
Agentic systems require thoughtful design. Speed matters less than safety and predictability in deployment.
Loved how you broke down the concept and highlighted the core technical distinction between a standard LLM and an AI Agent. Also, your agents are the cutest!
Thank you, Anna! 😀
Thank you, Ileana. This is an excellent post that really clearly explains what agents are and what they're not. I particularly liked how you broke down the ways in which different companies find agents and cut through the fluff to show that they're basically saying the same thing.
Thank you, Sam! I'm glad you liked it.