How I Taught an AI Agent to Save Its Own Progress

AI coding agents are stateless. Every time you start a new session, the agent has no memory of what happened before. If the session crashes, if you close the terminal, if context runs out — everything the agent knew is gone. I needed my agent to handle multi-hour development workflows. So I built a checkpoint system that lets the AI save and restore its own progress. The Problem With Long Workflows I use Claude Code for full development cycles — not just "write a function" tasks, but the whole thing: read a Jira ticket, write a design document, get it reviewed, implement across multiple modules, run tests, create PRs. That's a lot of steps. And any one of them can fail: The session crashes mid-implementation Context window fills up during code review I close my laptop and come back the next day A reviewer agent times out Without checkpoints, I'd restart from scratch every time. Read the ticket again. Regenerate the design. Redo work that was already done. What I Built I broke the devel