UK-based independent AI efficiency projectEvidence before claims ยท 2026

Shared AI Operating Rules: Reduce Repeated Decisions Without Promising Token Savings

A shared safeguard supplies a stable default while a task-specific contract retains evidence and acceptance criteria.

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Token Optimisation

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Classification: TOOL BEHAVIOUR is observable; token savings and behavioural improvements have not been measured in a paired test.

Shared AI Operating Rules: Consistency Without Savings ClaimsDESIGN PATTERN ยท EFFECTIVENESS NOT MEASURED. SHARED SAFEGUARDS โ†’ TASK CONTRACT โ†’ REQUIREMENT CHECK โ†’ BEHAVIOUR TEST. The article describes tool capabilities and a rule-design method; matched token and behavior outcomes are not available.DESIGN PATTERN ยท EFFECTIVENESS NOT MEASURED1SHARED SAFEGUARDS2TASK CONTRACT3REQUIREMENT CHECK4BEHAVIOUR TESTKeep stable constraints explicit ยท Bind rules to observable actions
DESIGN PATTERN ยท EFFECTIVENESS NOT MEASURED. The article describes tool capabilities and a rule-design method; matched token and behavior outcomes are not available.

A shared operating-rules file can make recurring safeguards easier to review and update. Its value is consistency and legibility until a matched test proves a reduction in request size or an improvement in accepted outcomes.

Keep policy short, scoped and testable

Rules work best when each line names an observable behaviour: persist a checkpoint, reconcile an ambiguous external write, separate process heartbeat from semantic progress, or stop at a human-only authority gate. Avoid broad slogans that do not tell a worker what to do at the next failure.

Reuse without blind repetition

A shared policy can provide stable defaults while each job supplies its own task contract, source evidence and acceptance test. Do not paste unrelated project history into every request. Retrieve specifics when they matter and pass forward a concise verified summary.

Before claiming context savings, preserve the old and new policy, count tokens using the same tokenizer or provider boundary, and test task quality on comparable cases. A line count is not a token count, and shorter instructions can omit a binding requirement.

See what each choice changes

The AI Agent Operating Rules Builder exposes recovery, checkpoint, idempotency, human-gate and progress choices as exact rule text. The Token Optimisation Rules Builder makes each selected sentence visible. Both run in the browser; neither measures how your agent behaves after installation.

For the evidence boundary between a short reply and a request, see the one-run canary report. For progress and recovery signals, read the incident observation.