Notes from the edge of enterprise AI.
Field notes on AI governance, secure intelligent systems, agentic patterns, and the operational discipline behind moving AI from pilot to production.
I Built a Framework So Disciplined I Couldn't Use It
I shipped a governance framework for AI agents, then failed its own adoption test — no uninstall, no way to list its skills, no way to know if it had drifted. Here's the sprint that fixed it, and the four patterns you can steal whether or not you ever touch my repo.
Inside My AI Operating System, Part II: The Console, the Leash, and the Memory It Keeps
My 3D AI office lied to me, and the afternoon I lost to it taught me more about governing agents than any amount of infrastructure did. Part II of the AI OS deep dive: telling a dashboard from a trigger, a leash on autonomy you can actually verify, and giving memory tiers.
Inside My AI Operating System: The Architecture Running My Agents 24/7
A technical deep dive into the always-on agent stack that runs my work: a Hermes kernel on my Mac, a Paperclip workforce on a VPS, one Obsidian vault as shared memory, MCP as the syscall layer — and a Tailscale mesh holding two machines together with no open ports.
My New Operating System: Hermes + Paperclip + Obsidian + MCP
I stopped thinking of my AI tools as separate apps and started running them like an operating system. Hermes is the always-on kernel, Paperclip is the agent workforce, a Jarvis wake-word loop is the microphone, one Obsidian vault is shared memory for every runtime, and MCP is the syscall layer.
The red thread problem: how skills, agents and governance rescue TOGAF traceability in agentic delivery
Agentic delivery generates plausible artifacts at every architecture layer with no enforced lineage. Here's how to keep the TOGAF red thread unbroken when agents are doing the work.
The latest evolution of skills.md isn't a better file — it's the runtime catching up to the prompt
Persona libraries, self-improving runtimes, and behavioural governance are three layers of the same stack. The frontier is making them work together.
Spec-Kit Best Practices Through a TOGAF Lens: An Architect's Playbook
Spec-Kit gives AI agents a disciplined workflow. TOGAF gives the enterprise a disciplined architecture. Map them together and you get governed, AI-native delivery.
Your AI agents are untrained. The bottleneck was never capability.
We keep waiting for smarter models. But the agents we already have fail for the same reasons junior engineers do — no plan, no proof, no memory. Capability isn't the constraint. Discipline is.
Why I made the pipeline mandatory — and the agents got better
Conventional wisdom says you give a capable agent room to work. I did the opposite: a fixed, non-negotiable workflow from brainstorm to finish. Constraint didn't slow the agents down. It's what made them trustworthy.
Teaching agents to learn from losing
Most agent setups make the same mistake twice — or twenty times. The most valuable thing I built into the dojo wasn't a skill. It was a loop that turns every correction into a rule the agent can't forget.
I Built a Full SaaS App in One Session with GitHub Copilot: Here's What Happened
How I transformed a Next.js landing page into a full serverless SaaS with Document Intelligence and Chat Your Data — in a single Copilot session.
Claude vs GPT in the Enterprise: An Honest Comparison from the Field
A practitioner's honest comparison of Claude and GPT models in enterprise settings — strengths, trade-offs, and when to use which.
Azure AI Foundry in Production: Patterns That Actually Work
Practical patterns for deploying AI models in production using Azure AI Foundry — from model selection to cost optimization.
AI-Native Delivery: Why Traditional Software Delivery Fails with AI Agents
Agile, Scrum, and waterfall weren't designed for AI-assisted development. We need an AI-native delivery methodology.
The Copilot Agents Dojo: A Behavioral Governance Framework for AI Coding Agents
Most organisations let AI agents loose with prompts and hope for the best. That's not an operating model — that's a risk. The Dojo changes that.
Stop Prompting, Start Architecting: Governing AI Agents at Scale
If your AI coding strategy still relies on prompts, you're leaving leverage on the table. Here's how top teams govern AI agent behavior at the repo level.