14 stories
PMs should be able to understand how every account experiences their AI agent. Customer Monitoring groups users into the customers they belong to and shows each account's sentiment, issues, use cases, usage, and cost, all backed by traces and evals.
confident-trace is our new Python and TypeScript SDK for tracing production AI applications. Here's why we're separating it from DeepEval and what that means for your stack.
Report Templates let you customize the reports Confident AI generates for your team. Build daily reports that dig into traces, identify where your AI agent is underperforming, summarize common usage patterns, and show the exact pages and sections you care about.
Everything you need to know about AI agent observability in 2026 — traces, spans, and threads; online and offline evals; production monitoring; and closing the feedback loop so failures never repeat.
A practical guide to human-in-the-loop workflows for AI agent evaluation: how SMEs review AI agent failures, align automated metrics, and improve evaluation datasets.
A practical LLM evaluation guide for startups: build a small dataset, use the 2 + 3 metric rule, run CI/CD evals, and grow coverage from production signals and human review.
Passing evals doesn't mean your AI agent works — it means your tests missed how it fails. Why output-based evals create false confidence and what to measure instead.
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.
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.
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.
A step-by-step guide to MCP evaluation: how to test MCP-based LLM apps and agents, measure tool use and task completion, and catch failures with DeepEval.
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.
RAG evaluation metrics — answer relevancy, faithfulness, and contextual relevancy — measure retrieval and generation quality, with working DeepEval code examples.
Most LLM evals fail because their metrics don't predict ROI. This playbook shows how to build outcome-based evals that correlate with real business KPIs and user value.
Showing 14 of 14 stories
You're all caught up.
Get the top AI news and tools straight to your inbox every week.
Weekly · AI news from Confident AI (DeepEval)
Join 15,000+ AI enthusiasts
