Writing about AI, infrastructure, and what actually ships.
total 138 · ~11.5h read · updated Jun 25
Stop relying on Claude to remember your rules. Hooks make policy enforcement deterministic—every time, no exceptions. Part 3 of the Claude Code Enterprise Stack series.
CLAUDE.md is your team's shared playbook—the single file where coding standards, architecture decisions, and Claude Code behaviors live. Done right, it eliminates context-switching and makes Claude Code predictably effective across your entire team.
Enterprise Claude Code isn't about picking the right model—it's about designing a configuration hierarchy that scales from individual workflows to organization-wide policy without friction.
When I first built Gluon on my Mac mini, I was solving a personal problem: monitoring Claude agents without losing my mind to tmux logs. But when teams join the picture, everything changes — security, governance, observability, and the fundamental role of the developer. Here's what production infrastructure for autonomous agents looks like.
How Gluon's Ralph Loop enables autonomous Claude execution with built-in safety rails — circuit breakers, multi-signal completion detection, and cost controls that scale from simple tasks to complex workflows.
Step into the cockpit of multi-agent orchestration — real-time monitoring, git isolation, cost tracking, and parallel workflows across 4 agents.
When orchestrating 4-5 Claude Code agents across projects, I was losing track of progress and cost. A 2-3 day build with Claude Code itself led to Gluon—an open-source orchestrator that treats AI agents like team members.
I built an open-source MCP server that reduces LLM token usage by 70-90% through server-side HTML filtering, markdown conversion, and CSS selector targeting. Here's why context efficiency matters—and how Scraper MCP solves it.
Most AI demos I'm shown answer the wrong question. They prove the model works; they never prove anyone needed it. Here's the builder-and-investor lens I use to tell the two apart before a cheque is written.
I've built the kind of agent framework AgentKit competes with. So when OpenAI shipped it two years "late," I knew exactly which problem they were actually solving — and which one they weren't.
I gave Claude Code an XML backup of my 19-year-old WordPress blog and asked it to rebuild everything as a modern NextJS site. What happened next was like watching a swarm of expert developers work in parallel—spawning agents, debugging TypeScript errors, and shipping production-ready code. All in 26 minutes. For eight dollars.
After watching 40% of agentic AI deployments fail in production, I'm building Dagentic — a serverless-first framework designed for what AI agents actually are: unpredictable, spiky workloads that modify themselves mid-execution.
