Shared technical guidance
What decision is the AI allowed to influence?: ai and intelligent systems application
Authority stays bounded. AI and intelligent systems applies this principle to approved business input, permission filter and model or retrieval task. Drafting a reply, finding a policy paragraph and switching a light carry different risks. We classify whether the output is advice, a suggestion, a reversible command or an action with material effect. The delivery timeline surveys approved business input before design approval, then tests model or retrieval task during commissioning. For this service, the scope records how human approval connects to logged business outcome, then verifies that relationship during commissioning or recovery testing.
Authority stays bounded. AI and intelligent systems applies this principle to prompt and tool config, evaluation and logs and business decision owner. Retrieval, tools and larger models may reduce some errors, but none turns generated text into verified truth. We design citations, confidence cues and human review around the actual consequence. Medical, legal, financial, employment, security and life-safety decisions require qualified owners outside the model. For this service, the scope records how model provider connects to prompt and tool config, then verifies that relationship during commissioning or recovery testing.
Where may data travel and remain?: ai and intelligent systems application
Authority stays bounded. AI and intelligent systems applies this principle to consequence level, data sensitivity and known baseline. It does not mean every connected feature is offline or that privacy appears automatically. Model downloads, telemetry, speech services, connectors, backups and support tools can cross that boundary. For this service, the scope records how review effort connects to non-AI route, then verifies that relationship during commissioning or recovery testing.
Authority stays bounded. AI and intelligent systems applies this principle to low-confidence output, block consequential action and route to human review. A knowledge assistant must not reveal a document merely because it indexed the words. Service accounts receive the narrow access needed for their task, secrets stay out of prompts and logs, and retention is set deliberately. The customer decides the lawful basis, staff policy and approved datasets. For this service, the scope records how use non-AI process connects to record failure case, then verifies that relationship during commissioning or recovery testing.
How is accuracy tested after launch?: ai and intelligent systems application
Authority stays bounded. AI and intelligent systems applies this principle to representative cases, denied request and missing evidence. Acceptance testing uses representative inputs, awkward phrasing, missing information, denied requests and known edge cases. The team records the expected outcome, model response, source citation, tool call and reviewer decision. Maintenance repeats the representative cases and denied request checks after material changes, updates or reported faults. For this service, the scope records how tool failure connects to reviewer decision, then verifies that relationship during commissioning or recovery testing.
Authority stays bounded. AI and intelligent systems applies this principle to prompt and tool config, evaluation and logs and business decision owner. Version records and a small repeatable test set make drift visible after an update. There is always a non-AI route for important work: manual control, ordinary search, a queue for staff review or a disabled automation. We prefer a narrow assistant whose limits are obvious to a broad agent with vague authority. A lower-complexity alternative remains valid when model provider can be handled by the documented manual or existing-system route. For this service, the scope records how model provider connects to prompt and tool config, then verifies that relationship during commissioning or recovery testing.
