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Soon, agents will be doing your work for you on Confident AI, on their own. We thought you'd like to meet them first.
Most teams sample 1% of production traffic for online evals and classification because every judge call costs money. Jev-as-a-Judge makes those decisions fast and cheap enough to run on 20%, 50%, or more of your traces, so every workflow on Confident AI has more data to work with.
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.
Filters only answer the combination you thought to ask. Category Correlation analyzes every pair of classifier labels across your incoming traces and threads, surfaces the relationships that matter, and links them back to the evidence.
Finding a production issue should not mean hours filtering traces. The Drift page automatically versions each agent configuration, surfaces anomalies and regressions, and shows exactly where each change is concentrated.
Voice AI teams should not need one tool for audio testing and another for general AI evaluation and observability. Voice AI Evals bring both together on Confident AI, from persona-based testing to deterministic audio metrics.
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.
The right evals start with real production failures, not a list of generic metrics. Here is a practical workflow for discovering issues, validating them with humans, and turning them into durable evaluation coverage.
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.
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.
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.
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.
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.
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.
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 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 guide to running LLM experiments across prompts, models, tools, datasets, metrics, production A/B tests, and human-in-the-loop feedback loops.
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 guide to human-in-the-loop workflows for AI agent evaluation: how SMEs review AI agent failures, align automated metrics, and improve evaluation datasets.
AI systems can pass evals while still behaving incorrectly. This post explores three common failure modes that slip through output-based evaluation.
AI systems can pass evals while still behaving incorrectly. This post explores three common failure modes that slip through output-based evaluation.
AI systems can pass evals while still behaving incorrectly. This post explores three common failure modes that slip through output-based evaluation.
AI systems can pass evals while still behaving incorrectly. This post explores three common failure modes that slip through output-based evaluation.
AI systems can pass evals while still behaving incorrectly. This post explores three common failure modes that slip through output-based evaluation.
AI systems can pass evals while still behaving incorrectly. This post explores three common failure modes that slip through output-based evaluation.
AI systems can pass evals while still behaving incorrectly. This post explores three common failure modes that slip through output-based evaluation.
AI systems can pass evals while still behaving incorrectly. This post explores three common failure modes that slip through output-based evaluation.
AI systems can pass evals while still behaving incorrectly. This post explores three common failure modes that slip through output-based evaluation.
AI systems can pass evals while still behaving incorrectly. This post explores three common failure modes that slip through output-based evaluation.