3 posts tagged with "AI MCP"

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.

Stop talking about AI "wrappers" Start talking about Multi-Agent Autonomous Routing.​

This week, I pushed my absolute limits as a Senior Software Engineer to solve a massive bottleneck in production AI deployments: token cost and model reliability.

Here is exactly what I designed, built, and shipped:

1️⃣ The OpenClaw Multi-Agent Routing Engine: Implemented a complex routing architecture supporting custom Model Context Protocol (MCP) servers. I built 6 specialized sub-agents running continuously 24/7 inside a single OpenClaw server, each completely owning an independent domain within the build and ship cycle.

2️⃣ Advanced Model Substitution (Ditching the Expensive Defaults): Instead of defaulting to heavy cloud APIs (like Claude Sonnet 4.6), I engineered multi-prompt MCP servers capable of executing repeatable, highly structured behaviors under tight circumstances.

3️⃣ The Results: By relying heavily on deterministic MCP code, we achieved identical operational performance to top-tier proprietary models while running on an open-source, modest backbone (GLM 5.1). Zero luxury cloud spend. Complete architectural independence.

This wasn't about writing basic API endpoints. This required deep architectural and decision-making orchestration around multi-agent prompting and state isolation. I had to build a system that manages multiple known operational scenarios flawlessly without forcing the core LLM to do the heavy cognitive lifting—keeping latency low and execution completely reliable.

If you aren't building runtime infrastructure that maximizes small, open-source models through hyper-engineered tooling, you're lighting your cloud budget on fire.

We don't wait for the future of agentic workflows. We ship it. 🛠️

Stop buying bloated 3rd-party software. Build what your architecture actually demands.​

This week, I didn't just ship features—I pushed my limits as a Senior Software Engineer by designing and launching custom AI-driven infrastructure to solve real organizational bottlenecks.

Here is a look at what went from my brain into production over the last 5 days:

🛠️ Custom AI RBAC System Instead of relying on rigid third-party access tools, I architected and built a native AI Role-Based Access Control (RBAC) application. It automatically reads and analyzes user permission levels, detects technical debt, and queues up precise fixes for engineering validation.

🌐 Advanced Model Context Protocol (MCP) Ecosystem I fully leaned into the MCP framework to supercharge our AI Agents:

• The Framework: Evaluated agent ecosystems and built a streamable HTTP MCP Server entirely in pure Node 24, replacing clumsy CLI commands with a robust package of REST API tools and custom prompts designed to aid weaker models.

• The Deployment Guardrail: Built an MCP Server that hooks directly into GitHub Actions. It assists AI agents in real-time configuration, troubleshooting, and deployment validations—enforcing strict, company-wide standards.

⚡ Multi-Cloud AI Clustering Architecture Designed and launched a Multi-Cloud AI Clustering MVP capable of running services across Kubernetes over disparate cloud providers, seamlessly connecting active AI agents to support the cluster.

The Reality Check: True senior engineering isn't about using the shiniest new model; it's about making AI work within constraints—building tooling that supports weaker models, automating manual deployment verification, and architecting custom solutions that eliminate external dependencies.

We don't wait for tools to be built for us. We build them.

What did you put into production this week?