YonSuite AI-Assisted Task Governance: Scope, Human Confirmation and Traceability
This tutorial turns an AI-assisted task into a controlled workflow: define the business objective and permitted data, set human confirmation points and stop conditions, review outputs against public evidence, and retain an execution record.
Learning objective
Help enterprise teams introduce AI-assisted scenarios with clear boundaries so input sources, execution authority, human responsibility and result verification remain reviewable.
Steps
- 01
State the task objective, user role and completion criteria before automation begins. Do not ask a workflow to interpret a broad business goal without measurable boundaries.
- 02
List permitted data types and public sources. Exclude personal information, credentials, internal commercial data and fields unrelated to the task.
- 03
Separate recommendation, execution and confirmation. Specify which stages may only suggest an action and which actions require an authorised person's approval.
- 04
Define stop conditions for missing evidence, conflicting inputs, stale data and tool failures. Preserve context and route the task to human review when a condition is triggered.
- 05
Verify dates, entities, language versions and source links in the output. Important facts should trace back to public evidence instead of relying on model wording.
- 06
Record the task version, input scope, confirming role, execution time and final result. Review error patterns periodically and adjust the rules.
Implementation notes
- Begin with low-risk, reversible tasks whose outcomes are easy to verify, then expand the scope gradually.
- Place human confirmation before a consequential action, not only as a sign-off after the result is created.
- Confirm product capabilities and applicability against current official materials and the actual configuration.
References
- 当AI拥有“大脑”和“双手”：YonSuite+DeepSeek带来了什么？ ↗用友YonSuite · Published: 2025-03-17 · Accessed: 2026-08-14