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Grok Bot+18 agentsAI Quality Lab — Test, Benchmark & Improve AI Agents
Build reproducible evaluations for AI agents with test sets, calibrated rubrics, failure analysis, regression checks, and evidence-based release gates.
$9.99
AI Quality Lab — Test, Benchmark & Improve AI Agents
Example session with this skill installed
Design an evaluation plan for our AI shopping assistant before a major prompt update. It answers questions about product features and compares options using a supplied catalog and policies. It must cite catalog evidence, ask when requirements are unclear, avoid unsupported availability or price claims, and never claim an order was placed unless a verified tool result confirms it. We have a small collection of reviewed conversations, but no labeled benchmark or measured baseline yet. Create a coverage map, golden-case schema, test set plan, dimension-based rubric, hard gates, evaluator-calibration approach, regression plan, and release decision template. Separate proposed tests from anything actually measured; do not invent results or thresholds.
- Read your context and instructions
- Compiled the ai quality lab
Summary
Build a versioned evaluation baseline before comparing the prompt update. No benchmark has been executed and no defect rate or release threshold was supplied, so every result below is a proposed test or decision rule—not a measured finding.
Quality contract
| Area | Acceptance rule | Evidence |
|---|---|---|
| Product claims | Material feature, availability, and price claims match the supplied catalog version. | Catalog row/version and cited passage |
| Grounding | Each required factual claim has relevant support; unsupported claims are flagged. | Claim-to-source map |
| Clarification | Missing requirements that change the recommendation trigger a targeted question. | Input ambiguity and response |
| Tool fidelity | The assistant reports an order only after a verified successful order-tool result. | Tool call and returned state |
| Task completion | Comparison addresses the buyer’s stated criteria and explains trade-offs. | Requirement checklist |
| Safety and privacy | No unauthorized data disclosure or action. | Redacted sample and trace, where available |
Test coverage map
Stratify cases across product category, routine vs. ambiguous request, English vs. supported languages, complete vs. missing catalog facts, conflicting policy/catalog data, unavailable items, price changes, citation requirements, and tool success/failure. Include normal, edge, negative, and adversarial cases. Add unanswerable cases to test abstention. Use reviewed conversations as seed material only after removing sensitive data and documenting provenance.
Golden-case schema
Fields: case ID; source; user job; input; context version; expected outcome; required claims and evidence; allowed variants; forbidden behavior; expected/allowed tools; risk; rubric version; reviewer notes.
Keep each case independent, version the catalog/policy snapshot, and prevent evaluation cases from becoming hidden prompt examples where feasible.
Rubric and evaluation
Score task success, correctness, groundedness, completeness, instruction adherence, clarification, tool selection/use, state claims, safety/privacy, and clarity separately on anchored 0–5 scales. A critical hard-gate failure overrides any average. Calibrate AI judges against a human-labeled subset; review disagreements, bias by product/language, and false positives before relying on judge scores. Use deterministic checks for required citations and claims about order completion.
Comparison and release process
Run the old and updated prompts on the same frozen cases. Blind and randomize output order where practical. Repeat stochastic cases when variability matters. Track pass/fail and defect counts with denominators, per-dimension distributions, critical failures, segment results, cost, and latency only when actually captured.
| Gate | Decision rule | Current state |
|---|---|---|
| Evidence validity | Cases and reference data are versioned and reviewed | Proposed |
| Critical constraints | No verified critical hard-gate failures under the agreed test scope | Threshold not yet agreed |
| Quality comparison | No material regression on required dimensions | Baseline not measured |
| Operations | Cost and latency limits are explicitly set and measured | Not provided |
Next steps
First confirm the catalog/policy snapshot, supported languages, high-risk actions, and decision owners. Label and review the initial cases, agree numeric thresholds from business requirements, then execute both prompt versions. Record actual runs and unresolved uncertainty before making the release decision.
Connects securely to your tools. The creator never sees your data.
About this skill
Turn AI quality claims into repeatable evidence
AI Quality Lab helps AI product teams, developers, QA engineers, and consultants design rigorous evaluation systems for prompts, assistants, RAG applications, tool-using agents, workflows, and multi-agent products. It evaluates the complete task—not just how polished an answer sounds—and keeps quality, safety, reliability, cost, latency, and business completion visible as separate dimensions.
Build an evaluation system
- Translate user jobs and critical constraints into a quality contract and measurable acceptance criteria.
- Map coverage across tasks, user segments, languages, difficulty, tools, retrieval conditions, permissions, edge cases, and risk.
- Design versioned golden cases with inputs, context, expected outcomes, evidence requirements, allowed variation, and forbidden behavior.
- Combine deterministic validators, reference checks, human review, pairwise comparisons, calibrated AI judges, and tool/state verification where appropriate.
- Classify failures such as grounding, retrieval, reasoning, tool selection, state, safety, privacy, format, latency, cost, or evaluation error.
- Plan baselines, blind comparisons, repeated runs, regression checks, release gates, and improvement experiments.
Useful outputs
An evaluation blueprint; test taxonomy and coverage map; dataset and rubric specification; scorecard; judge-calibration plan; failure taxonomy; experiment plan; regression ledger; release-gate proposal; and concise report with evidence status and limitations.
Use it well
Describe the product, user jobs, critical constraints, available prompts/data/results, supported tools, and release decision. The skill distinguishes proposed tests from tests actually executed and will not invent results, provider behavior, costs, latency, or statistical significance.
Boundaries
It does not automatically connect to production or execute a benchmark without the required tools and authorized artifacts. Current model/provider claims may require verification. Human review remains important for nuanced, safety-critical, or high-impact decisions. Do not optimize one score at the expense of a critical requirement.
How to use
Skills on Agensi run inside your AI agent. Copy the prompt, paste it into your agent, and it connects to Agensi and installs this skill for you. If a payment is needed, your agent shows it to you first.
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