EvalOps Cluster
EvalOps Cluster
Section titled “EvalOps Cluster”EvalOps is the operating discipline that keeps AI systems from relying on vibes. This cluster groups pages that turn examples, traces, labels, scorecards, incidents, and release gates into a maintainable quality system.
How to use this cluster
Section titled “How to use this cluster”Use this hub by the failure mode you are trying to prevent:
| Failure mode | Start here | What the page should help you decide |
|---|---|---|
| The team has demos but no release standard | What is EvalOps for AI teams? | Whether evaluation is owned by product, QA, platform, or engineering |
| Agent runs look good until a tool call fails | Trace grading for tool-using agents | Whether the whole run is being evaluated, not only the final answer |
| Scorecards are subjective | What should an agent eval scorecard measure? | Which outcome, policy, tool, and review metrics belong in the gate |
| Production quality drifts after launch | Live traffic sampling | How to sample real traffic without overwhelming reviewers |
| Releases depend on judgment calls | EvalOps release gates | Which failures block rollout and who can override them |
This keeps the cluster focused on operational evaluation, not generic benchmark commentary.
The quality floor for pages in this cluster
Section titled “The quality floor for pages in this cluster”Pages in this cluster should answer at least four practical questions:
- What artifact is being evaluated: answer, trace, tool call, retrieval result, policy decision, or final business outcome?
- Who owns the score: product, QA, platform, security, support operations, or engineering?
- What action follows a bad score: block release, route for review, retrain labels, change prompts, or roll back?
- How does the team prevent the same failure from disappearing into a spreadsheet?
If a page cannot answer those questions, it is probably too shallow for this cluster. EvalOps content should make a team better at shipping, not only better at discussing AI quality.
Foundations
Section titled “Foundations” What is EvalOps for AI teams? Start here to turn evaluation into release discipline.
EvalOps implementation roadmap Move from ad hoc evals into traces, datasets, scorecards, owners, and release gates.
What should an agent eval scorecard measure? Measure outcome quality, tool behavior, review burden, and policy risk.
Production evidence
Section titled “Production evidence” Trace grading for tool-using agents Grade the whole run before the final answer hides process failures.
Live traffic sampling Sample production traffic without missing risky slices or overwhelming reviewers.
Ground truth collection Build evaluation data from real production outcomes instead of benchmark theater.
Release and incident discipline
Section titled “Release and incident discipline” EvalOps release gates Use named owners, real gates, and explicit override discipline.
Shadow evals and canary rollouts Stage agent changes before wide release.
How to review AI agent production incidents Turn incidents into eval cases, alerts, release gates, and ownership changes.