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Three Ways AI Systems Fail Even When Evals Pass
AI systems can pass evals while still behaving incorrectly. This post explores three common failure modes that slip through output-based evaluation.
Brian Neville-O'Neill
Your AI Agent Passed Evals. That’s the Problem.
Passing evals doesn’t mean your system works. It means your tests didn’t catch how it fails.
Brian Neville-O'Neill
Launch Week Day 5 (5/5): Generate Datasets from Your Data Sources
Your best evaluation data already exists — it's sitting in Google Drive, SharePoint, Notion, and S3. Dataset generation on Confident AI turns your existing documents into evaluation-ready datasets automatically.
Jeffrey Ip
Launch Week Day 4 (4/5): Auto-Categorize Traces & Threads
You can't improve what you can't see. Auto-categorization tells you what your users are actually asking, detects response drift, and shows you which categories perform best — and which ones need help.
Jeffrey Ip
Launch Week Day 3 (3/5): Auto-Ingest Traces into Datasets & Annotation Queues
Production traces are the best dataset you’ll ever get — but most teams never turn them into one. With auto-ingest, your traces flow straight into datasets and annotation queues, continuously.
Brian Romain
Launch Week Day 2 (2/5): Scheduled Evals
Everyone agrees evals should run regularly. But nobody remembers to actually run them. Scheduled Evals fixes that — set the frequency, configure your mappings, and never scramble before a release again.
Kritin Vongthongsri
Announcing Launch Week Q1 '26! Day 1: Automated Error Analysis
Error analysis used to mean pulling traces in code, hacking together an LLM to recommend metrics, and hoping for the best. Not anymore.
Jeffrey Ip
Multi-Turn LLM Evaluation in 2026: What You Need to Know
In this article, I'll break down multi-turn LLM evaluation — how it differs from single-turn, what metrics actually matter, and how to implement it.
Jeffrey Ip
The Step-By-Step Guide to MCP Evaluation
This article will teach you everything you need to evaluate MCP-based LLM applications.
Cale
AI Agent Evaluation: Metrics, Traces, Human Review, and Workflows
A practical guide to evaluating AI agents with LLM metrics and tracing—plus when human review matters, how it calibrates judges, and workflows that combine CI, sampling, and production signals.