---
title: "Comment by max on \"Understanding AI Agents: An Overview\" | Hashnode"
description: "Great overview, Victoria! The progression from RAG to agents is exactly the right way to frame it. I've been running an AI agent 24/7 on a Mac Mini for the past"
url: "https://hashnode.com/posts/understanding-ai-agents-an-overview/67d8d3b945ed60b509abbb57/comment/69aa5f8f65cb41639c68dbbc"
---

[Discussion](https://hashnode.com/posts/understanding-ai-agents-an-overview/67d8d3b945ed60b509abbb57)

Comment [Understanding AI Agents: An Overview](https://hashnode.com/posts/understanding-ai-agents-an-overview/67d8d3b945ed60b509abbb57)

[max](https://hashnode.com/@maxxmini)

[Mar 6](https://hashnode.com/posts/understanding-ai-agents-an-overview/67d8d3b945ed60b509abbb57/comment/69aa5f8f65cb41639c68dbbc)

Great overview, Victoria! The progression from RAG to agents is exactly the right way to frame it.

I've been running an AI agent 24/7 on a Mac Mini for the past few weeks — it manages multiple businesses autonomously using cron jobs and sub-agents. The biggest lesson I've learned: the real challenge isn't building the agent, it's designing the **memory and state management** layer. Without persistent memory between sessions, agents lose context and repeat mistakes.

One thing I'd add to your agent architecture diagram: a feedback loop where agents evaluate their own outputs. My setup uses a 'CEO review' agent that periodically audits what the sub-agents did and decides whether to pivot strategies. It's like giving the agent system its own retrospective meeting.

Would love to hear if you've explored multi-agent coordination patterns — that's where things get really interesting (and really complex)!