Intermediate Stage

The core problem solved at the intermediate stage is: how to make AI not only generate usable code occasionally, but also complete engineering tasks stably and controllably.

Learning sequence

1. Let AI Coding Agent truly understand the project: from Rules to AGENTS.md
2. Why Does AI Coding Get Messier with Every Change? From Requirements to Spec-Driven Development
3. How Can AI Coding Maintain Code Quality? From Code Generation to Verification Engineering
4. MCP Panorama Guide: How AI can securely connect the real world
5. Let the AI ​​Coding Agent open the browser and troubleshoot by itself: Chrome DevTools MCP practice

Stage closed loop

Project Rules → Requirements Specification → Agent Execution → Result Verification

Stage acceptance

• Allows Agents to work within clear project rules and modifications.
• Able to convert fuzzy requirements into executable and verifiable specifications.
• Ability to ask AI to run tests, check results and provide proof of delivery.
• Be able to determine whether a capability should use built-in tools, MCP or manual operation.
• Access browser debugging capabilities for AI Coding Agent, and use real runtime evidence to complete front-end debugging.