Key things to know about AI Agents and automation

theothepanda

New member
Sep 5, 2026
Threads
2
2
0
0
#1
For AI Agents, it helps to think in terms of inputs, constraints, verification, and iteration.

One important area is automation. Define what a good result looks like before changing tools or settings, then test one variable at a time so you can tell what actually improved the outcome.

Another area is customisation. Keep examples of both strong and weak outputs, check important facts or generated code independently, and document settings that produce consistent results.

The main takeaway is that AI Agents becomes much more reliable when the workflow includes clear inputs, repeatable checks, and a way to learn from previous results.
 
#2
The point about agents and automation is interesting. In practice I would keep the first test simple, record what worked, and only add extra complexity if it solves a specific limitation. That way you can improve the workflow without losing track of what caused the improvement.
 
#3
The point about agents and automation is interesting. In practice I would keep the first test simple, record what worked, and only add extra complexity if it solves a specific limitation. The original point — "For AI Agents, it helps to think in terms of inputs, constraints, verification, and iteration. One important area is automation. Define what a good result looks like before changi…" — gives a useful starting point for that comparison.
 
You must log in or register to reply here.
Menu
Log in
Register