KamSRE
KamSRE Turns AI Quality Into Scorecards
8 min read
Kam AI
Product and research
5th Grade Summary
KamSRE watches the AI system like an operations team watches production software.
It checks quality, drift, freshness, latency, cost, and release risk.
The output is not only a chart.
It is a scorecard that tells the team what to fix next.
SRE for AI is not only uptime.
The system can be up and still wrong. It can respond quickly with stale data. It can pass generic tests while one workload gets worse. It can spend more money while producing lower-quality evidence.
KamSRE exists to make those risks visible.
What KamSRE monitors
KamSRE should watch several lanes:
- availability
- latency
- model cost
- route accuracy
- hot-read freshness
- source separation
- label volume
- fixture pass rate
- judge disagreement
- human-review backlog
- drift by workload
- release gate failures
Operational signals
- Signal
- Route health
- Example question
- Are market-shape prompts reaching the right route?
- Action
- Add route fixtures or resolver rules
- Signal
- Freshness
- Example question
- Are answers using stale hot reads too confidently?
- Action
- Block answer or show stale caveat
- Signal
- Source separation
- Example question
- Are sportsbook and prediction-market signals mixed?
- Action
- Fail deterministic source grader
- Signal
- Denominator quality
- Example question
- Are trend claims backed by game lists?
- Action
- Promote denominator fixtures
- Signal
- Label backlog
- Example question
- Are humans falling behind on high-value failures?
- Action
- Prioritize review packets
- Signal
- Cost drift
- Example question
- Did a workload become more expensive?
- Action
- Inspect model/tool routing
- Signal
- Latency drift
- Example question
- Did answer path time increase?
- Action
- Profile tool plan and caching
- Signal
- Release risk
- Example question
- Did fixtures regress in one workload?
- Action
- Hold release or scope fix
Takeaway: KamSRE turns hidden AI quality risk into work the team can prioritize.
Daily health digest
The daily digest should not be a pile of charts.
It should answer:
What got worse?
What stayed broken?
What improved?
What should a human review today?
What should block a release?
Visual artifact
Daily AI health loop
The digest connects production traces, labels, scorecards, and work items.
- 01evidence
Collect signals
Trace volume, failures, latency, cost, labels, fixtures, and judge evidence.
- 02scope
Score workloads
Compute health by workload instead of hiding problems in a global average.
- 03answer
Select review items
Choose high-value traces for labeling based on severity, frequency, and coverage gaps.
- 04answer
Create work
Open KamOps review packets, fixture candidates, or engineering tasks.
A digest is useful when it creates the right next action.
Workload scorecards
The workload scorecard is the operating unit.
Example workload health
Fixture pass rate
Healthy
Route accuracy
Watch
Freshness compliance
Needs work
Label backlog risk
Rising
Takeaway: One global number is not enough. Kam needs scorecards that show where quality is strong or weak.
Scorecards should show both status and evidence. A red card without examples creates anxiety. A red card with failed traces, labels, and fixture gaps creates work.
Drift for Kam is domain-specific
Generic drift monitoring is useful, but Kam has its own drift types:
- route drift
- workload mix drift
- source-family drift
- model-use drift
- cost drift
- latency drift
- label taxonomy drift
- denominator drift
- freshness drift
- answer-shape drift
Kam-specific drift
Route drift
The same user language starts landing in a different skill or fallback path.
Source drift
Answers increasingly rely on one source family or mix source families incorrectly.
Denominator drift
Aggregate trend answers become less auditable because supporting game lists disappear.
Takeaway: Drift should be measured against product contracts, not only statistical distributions.
The lesson
KamSRE makes the AI framework operable.
It turns traces into scorecards, scorecards into digests, digests into work items, and work items into fixtures or fixes. That is the difference between noticing an issue and running a quality system.
The next action is to make daily health digest output feed KamOps review queues and workload scorecards automatically.
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