Mnemosyne: Agentic Transaction Processing for Validating and Repairing AI-generated Workflows
arXiv:2607.00269v1 Announce Type: new Abstract: LLMs, solvers, and agent teams increasingly generate workflow actions, repairs, and plans, but a generated action may be syntactically valid yet stale, infeasible, conflicting, or destructive of the evidence that triggered a repair. We introduce Agentic Transaction Processing (ATP), a transaction model that treats generated actions as untrusted proposals until they pass deterministic admission under a declared, executable constraint set C. The principle is two-sided: a proposal is not truth, and no proposal foresees every disruption: anything may
The proliferation of LLMs and agent teams necessitates robust mechanisms to validate and repair their increasingly complex and autonomous outputs.
This development addresses a critical trust and reliability challenge in AI-generated workflows, moving towards more dependable and autonomous agentic systems.
AI-generated actions, repairs, and plans can now be subjected to a structured, auditable, and constraint-based validation process before execution, reducing errors and increasing safety.
- · AI developers
- · Enterprises deploying AI agents
- · SaaS providers
- · Compliance and risk management sectors
- · Platforms lacking robust validation
- · Developers of unreliable AI agents
- · Manual workflow management
- · High-risk, unvalidated autonomous systems
Increased adoption and trustworthiness of AI agents in complex, sensitive operational environments.
Reduced friction and higher efficiency in white-collar automation as agents can operate with greater autonomy and less human oversight.
Emergence of new regulatory frameworks centered around formal verification and transaction processing for AI-driven operations.
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Read at arXiv cs.AI