Architecting Real AI Production Systems
Architecting Real AI Production Systems 🛠️
Here is what went from my keyboard to production this week:
🚀 OpenClaw on Kubernetes: Took our multi-agent routing infrastructure off a single VM and containerized it into K8s. Why? Because manual infrastructure management is a waste of engineering time. We need scale on-demand, and now we have "Agents as Code."
🤖 Overcoming Weak Models: We are running a modest open-source GLM 5.1 model to handle 24/7 autonomous build/ship tasks. Weak models struggle in headless environments. Instead of throwing expensive cloud compute tokens at it, I engineered custom Model Context Protocol (MCP) servers to give it the tools, context, and discovery paths it needs to act autonomously without hand-holding.
🔍 Shipped: The SEO MCP Server: I built and deployed a new MCP server designed specifically to monitor 4xx proxy errors across our sites, hook into APIs, and actively allow AI agents to fix broken user paths before it impacts our SEO. It’s running right now over the weekend, fixing human mistakes proactively. The
Takeaway: Stop waiting for a "smarter" model to solve your complex workflows. Build the tooling, structure the autonomy, and coordinate the system heartbeats so your agents can run without you. If your infrastructure isn't working while you sleep, you're doing it wrong.