Summarize evidence
- AI may
- Draft a concise interpretation
- Authoritative layer
- Original approved sources
- Approval rule
- Human review when used externally
AI Solutions
Give the model a measurable task and keep authority outside it.
AI belongs in a product only when the task, approved context, evaluation method and escalation path are explicit. Price, permission, eligibility, publication and irreversible actions should not become probabilistic by accident.
Authority boundary
Model may propose
Authority / outcome
System or human decides
Authority map
What AI may do—and what it may not decide.
The useful boundary depends on consequence, reversibility and source quality.
Classify or prioritize
- AI may
- Score against defined criteria
- Authoritative layer
- Stored rules and record state
- Approval rule
- Escalate uncertain or high-impact cases
Draft content
- AI may
- Produce candidate copy
- Authoritative layer
- Editorial source + policy
- Approval rule
- Explicit publish approval
Recommend an action
- AI may
- Explain a possible next step
- Authoritative layer
- Price/permission/eligibility rules
- Approval rule
- No irreversible execution without approval
Permission model
Model capability is not permission.
The automation boundary is defined by business consequence, not by what the model can technically produce.
✓
Drafting, classification and summarization can be automated when their sources are bounded.
✓
Prices, permissions, publication, payments and other consequential states remain authoritative elsewhere.
✓
Human review is required where policy or business risk demands it.
✓
Every automated action needs a failure path that does not silently convert uncertainty into success.
Evaluation set
A useful AI feature is tested on the cases that can embarrass it.
Before production, create representative tasks that include normal inputs, missing context, conflicting evidence and adversarial instructions.
Expected success
Define what a sufficiently good answer must contain and what it must cite or preserve.
Expected refusal or escalation
Include cases where the model lacks evidence, authority or permission.
Regression set
Keep accepted examples so model or prompt changes can be compared before release.
Exception / boundary
A feature that cannot define acceptable failure behavior is not ready for autonomous action.
What this proves
The evaluation produces an inspectable release decision—not an invented “AI quality score.”
Production loop
Model output is a candidate inside a controlled workflow.
The production design matters more than the model demo.
01
Retrieve approved context
Limit sources and tools to what the task may legitimately use.
Output: Bounded context
02
Generate candidate
Produce the draft, classification or recommendation with traceable inputs.
Output: Candidate output
03
Validate or review
Apply deterministic checks and human approval where consequences require it.
Output: Accepted or rejected candidate
04
Act through an authorized layer
Only approved output can affect publication, records or other systems.
Output: Auditable action
Next step
Have a repeated task that looks suitable for AI but the risk boundary is unclear?
Define the task, evidence and decisions the model must never own. That is enough to determine whether AI should assist, recommend or stay out of the workflow.