FAQ · Methodology
How should rules, models, and people divide work in an AI workflow?
Use deterministic software for stable steps that can be explicitly coded; use models for unstructured understanding and bounded judgment; retain human authority for high-risk exceptions, accountability, and actions that are hard to reverse. The allocation should evolve with evaluation and operating evidence.
Key points
- Deterministic software: fixed calculations, validation, permission checks, state transitions, and explicit rules
- Models or Agents: understanding, summarization, generation, classification, and bounded recommendations that can be evaluated and reviewed
- Human authority: high-risk exceptions, value trade-offs, external commitments, payments, publishing, deletion, and risk acceptance
- Start with the cost of error and accountability boundary, not a target automation percentage
- Authority ladder: suggestion/draft → human confirmation → shadow mode → limited automatic action → broader authority after evidence
- If data, process, or model conditions change, authority can move back one level for revalidation
How to do it
- Map one real workflow step by step, including inputs, rules, exceptions, actions, and accountable owners
- Put repeatable, stable rules into deterministic workflow first
- Create real examples, refusal cases, exceptions, and safety evaluations for every model step
- Use human confirmation or shadow mode for high-risk actions before expanding authority