AI Ops Readiness Scorecard

Score your AI feature in 5 minutes against the same 6-component framework The Content Matrix uses with paying clients. Free.

Per 2026 industry analysis, AI agents with full evaluation coverage had a 9% production rollback rate over the prior year. Agents without had 47%. Same models. Same budget. 5× difference in whether the feature stayed up — explained almost entirely by the ops layer underneath. Source: digitalapplied.com 2026 enterprise data points · Fiddler AI 2026 production analysis
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Component 1 of 6

Eval Harness

Scoring AI output against representative inputs on every prompt or model change. The single most predictive component of whether a feature stays in production.

QUESTION 1
Do you have at least 10 representative input cases with asserted expected properties for this AI feature?
QUESTION 2
Do those eval cases run automatically on every prompt or model change?
Component 2 of 6

Verification Layer

Checking each factual claim against authoritative source data. The component that catches confident hallucinations before they ship.

QUESTION 3
Does every factual claim in your AI's output get checked against authoritative source data — not just the model's own recall?
QUESTION 4
Is that verification check independent of the model that produced the claim (separate model, retrieval, or rules-based)?
Component 3 of 6

State + Idempotency

Making sure retries and partial failures don't double-fire side effects. The component that prevents duplicate emails, double charges, and repeated posts.

QUESTION 5
Does every side-effecting action your AI takes (send, post, charge, write) have a unique idempotency key?
QUESTION 6
Can a retry of a partially-failed action be triggered without producing duplicate side effects?
Component 4 of 6

Cost + Rate Guards

Hard spend caps that fail the workflow closed when hit. The component that prevents the runaway loop billing four figures overnight.

QUESTION 7
Is there a hard per-run spend cap that fails the workflow closed when hit?
QUESTION 8
Is there a per-day or per-account spend cap with alerts firing before the cap is hit?
Component 5 of 6

Observability

Structured logs of every model call, tool call, and decision — enough to reconstruct any single run during a review.

QUESTION 9
Can you reconstruct every model call, tool call, and decision a single AI run made from your logs?
QUESTION 10
Are those logs structured (JSON) and queryable — not just stdout dumps?
Component 6 of 6

Approval Gate

Explicit human approval before any irreversible action — enforced in code, not just policy.

QUESTION 11
Does every irreversible action (publish, send, charge, delete) require explicit human approval before firing?
QUESTION 12
Is the approval gate enforced in code — not just in policy — so it can't be bypassed by mistake?
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    Sources for the 9% vs 47% claim: digitalapplied.com 2026 enterprise data points (analysis of 120+ AI agent deployments) · Fiddler AI 2026 ("AI Agent Failure Rate" production analysis). Same sources cited in our AI Ops Layer blog post.

    How this scorecard is used: It is the same 6-component framework The Content Matrix uses with paying clients to score AI features before they ship. Each component maps to a documented failure mode in the 2026 data.