Mnemosyne: Agentic Transaction Processing for Validating and Repairing AI-generated Workflows
LLMs increasingly generate workflow actions and repairs that may be well formed yet stale, infeasible, conflicting, or destructive of their own evidence. We introduce Agentic Transaction Processing (ATP), which treats generated actions as untrusted proposals until deterministic admission accepts them under an executable constraint set C. Its two-sided principle is: a proposal is not truth, and no proposal foresees every disruption. Anything may propose, but only the runtime admits and commits; unforeseen disruptions trigger bounded reactive repair whose output re-enters admission. Mnemosyne realizes ATP with an append-only transition log, effective-state projection, dependency-safe compen...
| Authors | Edward Y. Chang, Longling Geng |
|---|---|
| Primary category | cs.AI |
| Published | 30 June 2026 |
| Last updated at source | 31 August 2026 |
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