Recoverability as a System Primitive for Long-Horizon AI Agents
Quick summary
arXiv:2609.13672v1 Announce Type: new Abstract: 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 Restarting repeats completed work, but continuing from unverified or outdated progress can carry earlier errors forward.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, an
Key takeaways
- arXiv:2609.13672v1 Announce Type: new Abstract: 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- Restarting repeats completed work, but continuing from unverified or outdated progress can carry earlier errors forward.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.
Why it matters
The importance of “Recoverability as a System Primitive for Long-Horizon AI Agents” will be measured by what changes in practice. User behavior, access conditions, verifiable performance and responsible-use outcomes are the signals worth following.
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