Design AI-First Engineering Workflows Around Agents

Melvin Vivas · X post · 2026-05-13 · Open on X

Topics: AI Dev Tools & Productivity, AI Agents, Tool Use & MCP, AI System Design & Architecture · Level: intermediate

Summary

The creator says not to build a normal engineering workflow and add AI later. Instead, design the workflow from the start around agents that code, test, verify and deploy. He argues this is where engineering discipline becomes an advantage, and he calls it the moat of Cursor Cloud Agents.

Key points

• Start AI-first instead of adding AI to an existing workflow.
• Design the workflow so agents code, test, verify and deploy.
• Engineering discipline (tests, verification, CI/CD) is what lets agents work reliably.
• The creator sees this as the moat of Cursor Cloud Agents.

Resources mentioned

• Cursor · tool · x.com · free · recommended by both Bashiri Smith & Melvin Vivas
  AI-native code editor (VS Code with AI built in).
  Also in: GLM 5.3 and GLM 5.3 Flash now in Cursor (Melvin Vivas on X · notes), Grok Bot Can Now Hand Off Coding Tasks to Cursor (Melvin Vivas on X · notes), Why Plan Mode Still Matters in AI Coding Assistants (Codex, Claude, Cursor) (Melvin Vivas on X · notes), Why Plan Mode Still Matters in AI Coding Agents (Codex, Claude, Cursor) (Melvin Vivas on X · notes) and 121 more

Try this

• Design new engineering workflows around agents from the start, covering coding, testing, verifying and deploying.
• Build an agent pipeline that writes code, runs tests, verifies the results and deploys.