Recoverability as a System Primitive for Long-Horizon AI Agents
AI agents can be interrupted while editing files, calling tools, or carrying out multi-step tasks. Restarting repeats completed work, but continuing from unverified or outdated progress can carry earlier errors forward. A saved state is not necessarily a suitable place to resume. We introduce recoverability as a system primitive that makes reuse an explicit decision: select a supported starting point and a permitted recovery action, or withhold automatic continuation. Its behavioral contract binds that choice to supporting evidence, execution, and independent checks. A reference architecture connects persistence, validation, and control, with complementary runtime instances testing distin...
| Authors | Zhihui Zhang, Wei Liu |
|---|---|
| Primary category | cs.AI |
| Published | 12 September 2026 |
| Last updated at source | 12 September 2026 |
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