Building AI Agents That Know When to Stop
A practical design for bounded agent loops: finish when the work is good enough, compact before context degrades, and stop safely when progress stalls.
Field Notes
Articles & write-ups
Longer notes on the systems I build, the problems behind them, and the lessons that survive beyond a launch post.
Memory as the Missing Layer in Self-Improving AI Agents
A practical architecture for agents that turn experience into safer, measurable improvements through memory, sandboxed experimentation, evaluation, and replay.