Christopher McCraley

Most AI governance frameworks still focus on governing models. But in production environments, models rarely operate in isolation. They operate inside systems that look more like this: data → model → agent → tool → decision → action → outcome The moment an agent can trigger workflows, call APIs, or move money, the risk surface changes. The question is no longer just: “Is the model accurate?” It becomes: “Should this system be allowed to execute this action under current conditions?” That’s where most governance programs still fall short. They monitor models. They audit logs. They review policies. But they don’t control execution. What’s emerging is a new layer of infrastructure: AI Control Planes. Systems that manage identity, permissions, runtime observability, and decision traceability across agent-driven workflows. In other words: governing the decisions and actions AI systems take in production, not just the models themselves. This is where the next generation of AI governance will be built.

How to clone yourself into Claude in 48 hours: (even if you've never written a prompt before) Here's the full step by step guide 👇 🔹 STEP 1: Write your about-me.md Open a text file. Write 10–15 lines. → Who you are → What you do → How you communicate → What you never tolerate in your work Drop it in your Cowork workspace folder. This is Claude's first briefing. Every task starts here. 🔹 STEP 2: Write your CLAUDE.md Think of this as your standing orders. "Read about-me.md before every task." "Match my voice. Never sound generic." "Ask questions before you execute anything." Write them once. They apply forever. 🔹 STEP 3: Build Projects — not chats A Project is a persistent workspace. → Its own memory → Its own files → Its own instructions One for content. One for ops. One for research. Not a thread you scroll back through. A system that already knows where you left off. 🔹 STEP 4: Connect your tools Gmail. Google Drive. Google Calendar. Notion. Connectors let Claude reach into the tools you already use. Read your emails. Check your schedule. Pull your docs. This is where Claude stops being reactive. And starts being operational. 🔹 STEP 5: Install Skills Skills = repeatable workflows. One command. /weekly-report /content-brief /client-proposal Type /skill-creator. Claude builds it with you. Takes 10 minutes. Saves hours — every week. 🔹 STEP 6: Schedule it Pick your most repetitive task. Set a frequency. Walk away. Monday briefing. Weekly report. Inbox rules. Cowork runs it automatically. Even while you sleep. Here's what you've built: → A version of Claude that knows your voice. → Knows your preferences. → Knows your workflows. 1. Save this post 📌 (you'll come back to it) 2. Write your about-me.md today (15 minutes, one time) 3. Add one feature per day until the system runs itself Built once. Runs forever. No excuses. __ Bonus 🎁 Cyberman is the best place to get tech news, tips, tutorials and resources. Join today and receive: • AI Job Guide • AI Sidehustle Ideas • 70+ free AI courses and more 100% FREE 👉 cyberman.ai/join

A couple of weeks ago I went to visit my son following a keynote at a PE conference in Los Angeles. I said what do you want to do this afternoon, and he said “Let’s go for a beer”, so we walked with the dog a mile or so to a beautiful outdoor “pub” which happened to have craft beer and we sat outside, and just talked for 3 hours. I miss my kids that live 1000s of miles away from me and I really try to maximize the time we spend together. That 3 hours was one of my favorite recent memories. No agenda, no task to perform, just hanging out, great beer, enjoying each others company and talking about life, the universe, Elon and anything else that came to mind. My Dad, used to always want to “go for a pint”, and since I was first able to go to the pub with him at age 14 in England (just for Shandy of course) he would take my brother and I, or we would cycle together, to the pub to play bar billiards, snooker, darts or whatever, but really just to hang out and chat. He yearned for us to be that old from the time we were born, and when we finally made it, it became a regular thing. That continued until he died, this month, 4 years ago. In a busy world, where everyone is virtually connected 24x7, never forget that the human touch, the personal interaction, and love=time spent, are as true today as they have ever been, possibly more so. Some of the best ideas come from conversations like these, where one idea sparks another and so on. It’s easy to not find the time to spend time, but it’s always better if you do. Social Networks and News Networks pound our brains with “BREAKING NEWS” that we can generally do nothing about. Focus on people you love not being the product of the advertising revenue payments that fund them. You’ll have a better life, be more fulfilled and your family will be happier.

MCP, RAG, and Skills are not alternatives. They solve different parts of the same problem. Most people confuse them because they all involve "giving AI more capability." But they operate at completely different layers. MCP: The Connection Layer MCP is about connectivity. Your model can't talk to Slack, search engines, or databases on its own. MCP fixes that with a standardized protocol: → Query comes in → MCP Client picks the right server → Server fetches data or triggers an action → Everything gets sent back to the model Instead of building custom integrations for every tool, MCP gives you one clean interface that works across any agent that speaks the protocol. Use this when your agent needs to work with real-world tools and APIs. RAG: The Knowledge Layer RAG is about grounding. LLMs don't know your private data. RAG fixes that. → Convert your documents into embeddings → Store them in a vector database → Query comes in → retrieve the most relevant chunks → Send those chunks + query + system prompt to the model The answer is now based on your data, not just general training. No hallucinations. No outdated context. Use this when you need accurate answers from your own content — docs, PDFs, internal knowledge bases. Agent Skills: The Execution Layer Skills are about action. This is where your agent stops answering and starts doing. → Run Python code → Read and write files → Call APIs → Automate multi-step tasks The model decides: answer this, or use a skill? Then it executes and returns the result. Use this when your agent needs to take actions, not just respond. How they fit together: This is where most builders get it wrong. You don't choose one. You combine all three. → RAG gives your agent the right knowledge → MCP connects it to tools and systems → Skills let it execute tasks end to end That's why strong agents feel useful. They don't just answer. They fetch, decide, and act. One-line clarity: → MCP = connection layer → RAG = knowledge layer → Skills = execution layer Understand this separation and designing AI systems becomes way easier. Most people understand the difference between AI tools. Very few know how to govern them. If your AI systems make decisions, you need more than tools. You need a framework. In our AI Governance Framework course, you’ll build a complete system: inventory, risk assessment, oversight, and documentation you can use in your company. 15% off with code AIGOV15 until Apr 30 Explore the course: https://lnkd.in/g8ST7XHF #AIcourse #AIcheatsheet #AIcommunity #AI

New Chapter: I am thrilled to share that I've joined #JPMorganChase as the lead for Firmwide Resilience and Simulation Oversight within the Compliance, Conduct, and Operational Risk (CCOR) organization. My onboarding experience has been seamless, blending incredible efficiency with a warm, personal touch that truly reflects the Firm's leadership culture. Saying goodbye to #MorganStanley was a difficult decision. Over the last 5 years, I've been challenged, mentored, and shaped by some of the best in the industry. I will deeply miss my friends and colleagues there, but I'm ready for the new mountain to climb. Huge thanks to my family, friends, and mentors who supported me through this transition-and to CCOR leadership for this incredible opportunity!

Goldman Sachs said it quietly; Massive AI investments. Surprisingly limited productivity gains. The bottleneck isn't the technology. It's the leadership team being asked to think differently, decide faster, and adopt new ways of working - simultaneously, under pressure - with no support for the human system running all of it. The prefrontal cortex is the first thing to degrade under sustained stress. ... No AI tool fixes that. When did you last invest in the system doing all the thinking? Leadership development and change management, rebuilt on how the brain actually works. #BrainCapital #Neurosience #LeadershipDevelopment #AugmentMind