Evaluate AI agent skills before they reach production.
A skill that reads well is not necessarily a skill that works. Skill evaluation turns a proposed instruction change into measurable evidence, including what improved, what regressed, and whether the new version is safe to approve.
Published · Lattix Technologies Corp.
What does an AI skill evaluation measure?
AI skill evaluation measures whether a reusable agent instruction produces the intended behavior across representative tasks. It is broader than checking whether a response looks plausible. A good evaluation tests the output against a clear rubric, the workflow's constraints, and the risks of taking the wrong action.
Because model outputs vary, results should be interpreted as evidence from a defined model, version, dataset, and evaluation configuration. They are not universal guarantees of future behavior.
Task success and answer quality for realistic cases.
Grounding and policy adherence, including prohibited actions.
Tool-use appropriateness and handoff correctness where observed.
Latency, token use, and cost where reported by the execution provider.
Design a credible test set
Start with real work patterns that can be sanitized and reproduced. Include ordinary requests, ambiguous inputs, adversarial instructions, and edge cases that previously caused failures. Define the expected outcome before evaluating a candidate to avoid changing the rubric to reward the result.
Separate development examples from a holdout set so repeated tuning cannot simply memorize the evaluation. Pin datasets and skill revisions to make a comparison reproducible.
Positive cases: the skill completes its intended task.
Negative cases: unauthorized or unsafe requests are rejected appropriately.
Regression cases: past production failures become permanent checks.
Holdout cases: withheld scenarios test generalization beyond the tuning set.
Compare candidates against a known baseline
Evaluate the currently approved version and the candidate under the same model, task mix, and scoring configuration. Report changes by dimension, not only a single blended score. A small average improvement may conceal a severe failure in a high-risk case.
Keep the exact test inputs, model configuration, judge rationale, and observed run evidence when authorized. Judge-model scores should be calibrated against human review and deterministic assertions where possible; a model's self-reported confidence is not proof.
Use paired comparisons so cases and conditions remain equivalent.
Distinguish observed model or tool traces from inferred or missing telemetry.
Treat critical safety regressions as release blockers even when the aggregate score improves.
Surface uncertainty when sample size, model drift, or judge reliability limits the conclusion.
Turn evaluation results into an approval gate
Define thresholds before running the candidate. A release decision can combine rubric minimums, zero-tolerance failure categories, stability requirements, and a cost budget. Store the gate outcome with the source revision and reviewer decision.
Savant's evaluation and release workflows are designed to make this evidence reviewable. A human remains responsible for approving a release; an optimization suggestion should never silently replace a production skill.
Pin the candidate and baseline to immutable source references.
Capture a clear pass, fail, or insufficient-evidence decision.
Block promotion on specified critical regressions.
Record approval, distribution state, and a rollback path.
Frequently asked questions
Is AI skill evaluation the same as security scanning?
No. Security scanning looks for hazardous instructions, suspicious dependencies, or other known risks. Behavioral evaluation tests how a skill performs against representative tasks. Both are useful and neither replaces the other.
Can an LLM judge other LLM outputs reliably?
An LLM judge can scale rubric-based assessment, but it can be biased or inconsistent. Use clear rubrics, deterministic checks when possible, calibrated human review, and pinned evaluation conditions.
How many cases are enough for a skill evaluation?
There is no universal minimum. Choose cases based on task variability and failure impact, and increase coverage for consequential workflows. Track what the dataset does not test instead of inferring broad reliability from a small pass rate.