---
name: tai-ch059-observability-and-tracing-for-ai-systems
description: 'Apply chapter 59 of Testing AI, Observability and Tracing for AI Systems, as a workflow for evaluating AI and non-deterministic systems. Use for test planning, eval design, quality review, release evidence, examples, or coaching related to observability and tracing for ai systems.'
---

# Observability and Tracing for AI Systems

Skill name: `tai-ch059-observability-and-tracing-for-ai-systems`

Based on **Testing AI: Engineering Confidence in AI Systems** by **Jason Arbon**.

## Purpose

You cannot debug a final answer if you cannot see the path that produced it.

## Use This Workflow

- Identify the AI behavior or release decision being evaluated.
- Define realistic cases, slices, unacceptable outcomes, and evidence needed for confidence.
- Choose measurements that match the risk: rubric scores, samples, intervals, traces, human review, deterministic checks, or production monitors.
- Report uncertainty, severe failures, and decision impact instead of only a pass/fail result.

## Key Guidance

Observability is the evidence trail for AI systems. It captures prompts, retrieved context, tool
calls, model responses, judge scores, token counts, cost, latency, errors, and user-visible
outcomes. For example, an agent may give a wrong refund answer because retrieval missed the
newest policy, a tool returned stale account data, the model ignored a permission rule, or a
downstream service timed out. The final answer alone does not tell you which layer failed.

## Apply The Approach

Create representative cases, score them with explicit criteria, review severe failures separately, report uncertainty, and connect the evidence to a concrete decision.

## Expert Notes

At expert level, traces should have stable correlation IDs, privacy-aware redaction, span-level
metadata, model and prompt versions, retrieval snapshots, tool inputs and outputs, token/cost
metrics, latency percentiles, judge scores, and links back to eval cases and production
incidents.