Explains composing service levels from components
Availability in series is the product of components; freshness follows the batch; component windows cover the service's.
Can AI do this?
AI does this well Most of the tested AI models master this. 4 of 4 models in the current benchmark master it.
| Model | Tier | Probability of mastery | Status |
|---|---|---|---|
| gemini-4-argon | Flagship | 100% | Mastered |
| gemini-3.5-flash-lite | Low cost | 100% | Mastered |
| gpt-6-luna | Low cost | 100% | Mastered |
| claude-haiku-5-5 | Low cost | 99% | Mastered |
Mastered means a probability of at least 90% that the model has this knobit, estimated from its graded answers. “Not shown” means the answers do not settle it either way. How the models are tested.
What it looks like in practice
Expected result: Availability in series is the product of components; freshness follows the batch; component windows cover the service's.
How it is assessed: Answers a written question on the service level composition correctly
Related knobits
Builds on
- O-651 Explains measurable service levels
- Q-1804 Explains the concept of a percentage
- O-1903 Explains why the availability of components in series is the product of their availabilities
Needed for
- O-604 Recognises OLAs unable to support the SLA
- O-605 Recognises freshness promised before the batch ends
- O-609 Recognises an OLA window narrower than the SLA's
- O-631 Composes availability across a dependency chain
All 13 knobits of Define SLAs and OLAs.
Source: Practice the stage 6 reference and review checklist rest on