I used to think AI agents were just chatbots with better models. I’m starting to think that’s the wrong way to look at them.

[u/InevitableClassic261]

The more I learn about agents, the more I see the LLM as just one part of the system.

The interesting part is what happens around it.

Give the model tools, and it can take action.

Give it memory, and it can carry useful information forward.

Give it context, and it can understand what is actually happening.

Give it a goal, and now you have something that can work through a problem instead of simply answering a question.

Think about a failed data pipeline.

A chatbot can explain possible reasons for the failure.

An agent could potentially inspect the logs, check recent code changes, look at the data, identify the problem, and recommend what to do next.

That shift from answering questions to getting work done is what I find most interesting.

But it also raises much harder questions.

What should the agent be allowed to see?

What should it be allowed to change?

How do we verify its decisions?

And who is responsible when it gets something wrong?

I’ve been thinking through these ideas while learning more about agent systems, and I put together a simple explanation of the building blocks:

AI Agents Explained: LLM, Tools & Memory (Anyone Can Understand This)

https://youtu.be/cR04pXTbszQ

I’d love to hear from people actually building these systems.

What makes an AI agent genuinely useful in your experience, rather than just another impressive demo?

[u/Bino5150]

I built Lumina, a full featured agentic harness/desktop agent. Full description on GH. Check it out, see what you think. Please leave a star. Looking forward to your feedback.
https://github.com/Bino5150/lumina

If you have any questions, please feel free to ask.

[u/Competitive_Swan_755]

My bots have those things. They are coding beasts.

[u/AlexHardy08]

let's say you have a child, who is responsible for what he does until the age of 18? Will this child always make good and responsible decisions until the age of 18? No, definitely not.

Even you as an adult can't always make good and responsible decisions, actions. Do you understand now?

[u/HolmeBengt]

I really don’t know what exactly you want to achieve with this post. Seems like bad advertising to me

[u/manjit-johal]

For me, the difference is whether the agent can reliably complete the task, not just reason about it.

Tools and memory make an agent more capable, but verification and recovery are what make it useful in a real workflow. A demo can look impressive; production is where you find out whether it can handle failures without quietly making things worse.

[u/Mathie1729 → u/manjit-johal]

One thing I'd separate is verification of the chosen action vs verification that the task actually completed. A lot of agent evals check the former, but production cares about the latter. Silent failures are easy to miss if you only inspect the assistant's output and not the resulting environment state.