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)!
Starting with the RAG limitation of vanilla LLMs before introducing agents as the orchestration layer is a solid teaching progression — it makes the jump from retrieval to autonomous tool use intuitive.
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)!