How does production evaluation differ from offline testing?

Offline evaluation is typically comprehensive and blocking, run on fixed test sets. Production evaluation uses asynchronous, non-blocking sampling of real traffic to provide live observability without slowing down the user experience.

What sampling strategies are supported?

The skill supports random sampling, stratified sampling (based on user tiers), and error-biased sampling which prioritizes the evaluation of errors, edge cases, and high-cost tasks.

How do I control the cost of using LLMs for evaluation?

It provides an evaluation budget management layer and uses a tiered approach, running cheap rule-based heuristics for high volumes and reserved LLM-as-judge evaluations for specific samples.

Can this skill help me detect performance drops?

Yes, it includes a Regression Detection system that compares current performance metrics against historical baselines, allowing you to trigger alerts when quality falls below a specific threshold.

AI Production Evaluation Strategy

Name: AI Production Evaluation Strategy
Author: nexus-labs-automation

bynexus-labs-automation

Analytics & Monitoring

Optimizes AI agent performance in production through strategic sampling, async evaluation pipelines, and regression detection.

About

This skill provides Claude with advanced strategies for monitoring and evaluating AI agents in live environments without impacting latency or operational costs. It enables sophisticated sampling techniques—ranging from random and stratified to error-biased sampling—while implementing non-blocking async evaluation queues. By leveraging LLM-as-judge heuristics and baseline comparisons, it helps developers detect quality regressions, manage evaluation budgets, and ensure consistent agent behavior across diverse production traffic.

Key Features

Multi-tier evaluation types combining fast heuristics with LLM-as-judge
Advanced sampling strategies including random, stratified, and error-biased methods
Integrated evaluation budget management to control operational costs
Non-blocking asynchronous evaluation pipelines for real-time traffic analysis
0 GitHub stars
Automated regression detection using historical baseline comparisons

Use Cases

Monitoring production agent accuracy and factuality across different user segments
Managing LLM evaluation costs by sampling specific percentages of high-stakes traffic
Detecting quality regressions immediately after deploying model or prompt updates

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add nexus-labs-automation/agent-observability production-eval-strategy

For use in Claude.ai and ChatGPT

Download Skill

GitHub