agent-evals-and-observability

Design and execute framework-neutral evaluation and observability methodologies for AI agent systems.

40|6|Updated Jul 11, 2026

Search Tags:#evaluation #observability #regression #telemetry #privacy #release-gate

Ready to run

npx skills add https://github.com/magnus919/agent-skills --skill agent-evals-and-observability

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: agent-evals-and-observability
Source: https://github.com/magnus919/agent-skills/tree/main/agent-evals-and-observability
Command: npx skills add https://github.com/magnus919/agent-skills --skill agent-evals-and-observability

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) and templates (resource) components.

What problem does it solve?

This skill addresses the gap between demo-quality agent performance and production-ready reliability by providing a framework for rigorous evaluation, trajectory review, and risk-tiered release gates.

Core Features & Use Cases

Evaluation Design: Define task and trajectory contracts to measure agent behavior against specific criteria rather than vague metrics.
Release Gates: Implement risk-tiered decision frameworks that separate hard safety invariants from statistical quality indicators.
Observability: Instrument agents with privacy-aware telemetry that provides diagnostic context without exposing sensitive user data or prompts.

Quick Start

Ask the agent to create an evaluation plan and release gate for the current agent change.

Frequently Asked Questions about agent-evals-and-observability

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I design an evaluation framework for AI agent systems?▼

Design an AI agent evaluation framework by defining task and trajectory contracts that measure agent behavior against specific criteria. This process includes dataset versioning, grader calibration, and trajectory review to ensure production-ready reliability.

How do I add observability and telemetry to AI agents without exposing sensitive data?▼

Add observability to AI agents using privacy-aware telemetry that provides diagnostic context without exposing sensitive user data or prompts. This instrumentation captures trajectory data and behavioral metrics for production-incident learning while maintaining privacy.

What is a trajectory contract in AI agent evaluation?▼

A trajectory contract in AI agent evaluation defines the specific behavioral criteria and steps an agent must follow during task execution. It replaces vague metrics with measurable benchmarks for grader calibration and trajectory review.

Can I use this evaluation methodology for different agent architectures?▼

Yes, this evaluation methodology is framework-neutral and satisfies requirements for statistical rigor across diverse agent architectures. It covers the full lifecycle from task contract definition to production-incident learning without tying to a specific platform.