The moment your AI agent starts a long-_running task, something inevitable happens: it forgets what it was doing. You see this pattern everywhere: A code review agent that loses track of which files it has already reviewed A research agent that stops mid-deep_dive because context window fills up A multi_step agent that completes step 3 but has no idea what step 2 produced This isn't a memory problem. It's an architecture problem. The Context Debt Problem Every agent accumulates context debt — the gap between what it knows and what it needs to know. Three layers cause this: Working memory — What the agent holds in its active context Episodic memory — What it remembers from previous turns Shared memory — What other agents know but this one doesn't When any layer fails, the agent loses continuity. It either: Repeats work it already did Misses context from a previous agent Hallucinates missing information The Memory Checkpoint Pattern The fix is simple but rarely implemented: checkpoint_based memory .…