Cool idea to help with AI governance enforcement. I just wrote a white paper about how to figure out the AI governance rules your organization needs for any AI features it enables. This is totally free, doesn’t ask for your email. It’s a novel adoption of Kimball’s Data Governance approach that has 30 years of success behind it. Turns out AI governance is an extension of this methodology.
I actually spent a lot of time doing agentic AI before any agentic AI coding tools were available. I was building teams of agents and having them work together through copy/paste when we just had chatbots. I learned things from that exercise that still hold true today. One of the most valuable I learned is this: when using your agents to do the initial big plan for your project, have the agent who most closely matches the domain of your project create the initial plan. Then serially review the plan with your remaining agents one at a time, incorporate their feedback before sending to the next agent for review. This gets you a much greater work product than if you had them analyze and provide feedback in parallel (results in AI slop).
1000% accurate. Planning is the difference. I just wrote a white paper that supports this concept at a more macro level. It shows the direct link between failure to plan with adequate business requirements is the exact reason something like 90% of Enterprise AI initiatives failed. Have a look and let me know your thoughts (no asking for your email or any of that) encephalon.net/whitepaper
encephalon.net/whitepaper