Once you've played around with AI for any length of time and got under the skin of LLMs, you inevitably start to uncover the quirks, weaknesses and vulnerabilities inherent to the fundamental design concepts, and the execution itself. As seen in my other AI projects on the site, it's not hard to trigger those weaknesses once you get beyond basic GenAI and into genuine design and build scenarios.

After creating a few AI agents, orchestration is the obvious next step; you get those agents talking with each other and working collaboratively, you go from having an assistant to having a team at your disposal. This requires an orchestration framework to connect the agents - I opted for LangGraph for this project due to it working on a state-machine basis, allowing cycles and loops where the agents could get into critique and refine loops with each other.