Stop Using Claude Wrong: My Production-Grade Agentic Stack (2026)

Most developers are using AI tools like it's still 2024. They paste code into a chat window, wait for a response, paste it back, and fix the syntax errors.

I don't use AI to write boilerplate. I use it to architect systems.

While claude-code is impressive out of the box, the default configuration feels like driving a Ferrari in a school zone. As a full-stack engineer shipping indie projects and managing complex Go/TypeScript backends, I need an agent that understands context, manages its own memory, and refuses to generate generic "AI slop."

Here is the exact stack, configuration, and reasoning behind my agentic workflow in 2026.

1. The Engine: Z AI

Direct API usage burns money and adds latency. I route everything through Z AI (Zhipu AI)—a platform providing GLM models with an Anthropic-compatible API endpoint.

The beauty of Z AI is that it's a drop-in replacement. Configure once, and Claude Code works exactly the same—but with GLM-4.7 handling the heavy lifting.

The config:

// ~/.claude/settings.json
{
  "env": {
    "ANTHROPIC_BASE_URL": "https://api.z.ai/api/anthropic",
    "ANTHROPIC_API_KEY": "your-zai-key"
  },
  "model": "opus",
  "alwaysThinkingEnabled": true
}

Model mapping (Claude names → GLM models):

2. The Brain: MCP Servers

The Model Context Protocol (MCP) is what separates a chatbot from a co-worker. It gives Claude eyes and ears inside your development environment.

Without MCP, Claude hallucinates based on training data from months ago. With MCP, it looks at reality.

My Must-Have Triad

Context7 in Action

When integrating a new library, I don't copy-paste docs:

>> /resolve-library "stripe-node"
>> /query-docs "how to handle webhook signatures in v16"

Claude reads the actual current docs and writes the implementation correctly the first time.

3. Custom Skills

This is where the setup gets opinionated. I've written custom skill definitions to force Claude to adhere to my engineering standards—not the "average" of the internet.

The Anti-Slop Frontend Designer

Most AI-generated UI looks identical: boring rounded corners, generic blue buttons, and "Inter" font. I built a skill to fight this.

# ~/.claude/skills/frontend-design
name: frontend-design
description: Create distinctive, production-grade frontend interfaces
 
instructions: |
  - REJECT generic AI aesthetics (bootstrap-style layouts)
  - USE "brutalist" or "Swiss" design principles unless specified
  - TYPOGRAPHY: Commit to bold choices. No Roboto. No Inter.
  - CODE: Accessible (ARIA), responsive (mobile-first), strictly typed
  - PREFERENCE: Tailwind v4 over CSS-in-JS

The Senior Engineer Code Reviewer

I don't need an AI to tell me about indentation. I need it to spot architectural flaws that wake me up at 3 AM.

# ~/.claude/skills/local-code-reviewer
name: local-code-reviewer
description: High-signal security and performance review
 
instructions: |
  Focus ONLY on critical failure points. Ignore style nits.
 
  1. Security: SQLi, XSS, IDOR, improper access control
  2. Performance: N+1 queries, memory leaks, unnecessary re-renders
  3. Concurrency: Race conditions in Go routines or Node promises
  4. Database: Missing indexes on foreign keys

The Workflow

Here's what a typical feature implementation looks like. Let's say I'm building a webhook handler:

Final Thoughts

The difference between a 10x developer and a 1x developer in 2026 is tool mastery.

By pointing claude-code at Z AI and giving it the right MCP tools, I've stopped treating AI as a search engine and started treating it as a Senior Staff Engineer that lives in my terminal.

If you aren't customizing your agent, you're playing on hard mode.

Check out the MCP Protocol to write your own server.