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Agent-Centric (智能体为中心)

Humans focus on goal decomposition and environment construction, while agents handle the heavy execution work.

Description​

The human role transforms from coder to architect and environment builder. AI not only writes business code but also manages CI/CD, monitoring dashboards, and other meta-code assets. Agents have local observability stacks and UI control, with custom linters enforcing architectural boundaries. The codebase itself becomes the agent's knowledge source, using "directory maps + progressive disclosure" patterns.

This represents the qualitative shift from AI as tool to AI as partner.

Key Signals​

  • ✓ Agents independently complete end-to-end feature development
  • ✓ Architectural constraints enforced through Agent Linter
  • ✓ Codebase optimized for agent readability

Key Investments​

  • → Create AGENTS.md documentation for agent usage guidelines
  • → Develop custom Agent Linter to enforce architectural constraints
  • → Optimize codebase structure for agent readability
  • → Build agent-specific observability toolstack
  • → Implement agent access to DevOps tools
  • → Design "directory maps" and progressive documentation disclosure

Metrics​

  • 📊 Agent independent task completion rate (target >70%)
  • 📊 Architecture violation auto-detection and blocking rate
  • 📊 Agent average task completion time
  • 📊 Human intervention frequency (target 50% reduction)
  • 📊 Codebase readability score

Next Level​

When agents can reliably complete end-to-end development with minimal human intervention, you're ready for Agent-First autonomy, where agents become the primary developers.