Persistent AI Workspace
Treat documentation, handover, and reusable skills as product information architecture rather than post-delivery paperwork.
New AI sessions often lose project context, validated decisions, and prior mistakes, forcing repeated exploration.
Layered context, workflow hooks, and Maker/Checker collaboration make remembering, reviewing, and consolidating part of daily delivery.
The practice record covers trajectory mining across 196 sessions and turns the learning into reusable skills and a maintenance loop.
Practice notes
- AI Agent Self-Evolution: Mining 196 Sessions into Reusable Skills — Architecture Deep-Dive
- Project Documentation for AI Agents: Agent Workspace RefArch in Practice
- Persistent AI Code Teams: Maker/Checker Architecture in Production
- AI Agent Amnesia: Every New Session Starts From Zero
- I Built a Memory System for My AI Agent: From ACE Paper to Production-Grade Loop
- Agent Self-Evolution: From Half a Year of Practice to a Trajectory Mining Pipeline