Certified Conversion Networks for AI Workflows

Abstract

This preprint formalizes AI-integrated workflows as certified conversion networks that turn candidate outputs into accepted, authorized, reproducible, maintainable, and safely deployable value. It models services, validators, reviewers, audit processes, memory, authorization, release, rollback, maintenance, and incident response as constrained edges or evidence channels, then uses typed evidence ledgers, contracts, witnesses, bottleneck prices, hard-gate certificates, queue stability, Goodhart budgets, and hazard charges to guide resource allocation.

Keywords

• AI workflows
• certified throughput
• robust certified value
• AI-integrated workflows
• workflow optimization
• compound AI systems
• AI agents
• long-running AI systems
• robust optimization
• evidence ledgers
• evidence contracts
• verification witnesses
• off-policy evaluation
• queue stability
• bottleneck analysis
• Goodhart's law
• AI governance
• deployment certification
• resource allocation

Identifiers and source records

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