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The latest developments, best practices and deep dives on AI evals.
A Practical Guide to Building the Right Agent Evals
Confident AIA Practical Guide to Building the Right Agent Evals
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.
Introducing Report Templates: Build the report your team actually reads
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.
AI Agent Observability: Everything You Need to Know in 2026
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.
Introducing Synthetic Data Generation Pipelines: Customize how you generate data
Many teams already had great synthetic data generation pipelines running locally, but consolidating that work on one platform usually meant giving up flexibility. Synthetic Data Generation Pipelines bring that control into Confident AI: choose the sources to draw context from, wire them together, and tune each generation step.
Introducing Annotation Forms: Capture any human feedback without leaving Confident AI
Human review only helps if everyone captures the same thing. Annotation Forms let you define the exact set of fields reviewers fill in — text, numbers, scales, yes/no, single and multiple choice, and scored criteria — so every annotation comes back structured, consistent, and ready to act on.
Introducing AI Observability Workflows: Custom automations for every trace on the platform
Dataset ingestion, queue ingestion, evaluation rules, and classifiers have lived on Confident AI for a while — but in separate corners of the product. Workflows brings them into one interface: a single graph of your post-ingestion pipeline, with a tab to configure each task. Here's how it works.
Introducing AI Governance: Standardized evals, policies, and controls
As AI spreads across an org, every team evaluates differently and no one can answer 'is this ready to ship?'. AI Governance is the layer on top of the evals, observability, and red teaming your teams already run — turning those signals into one standard, enforced at deploy time.
Human-in-the-Loop Workflows for AI Agent Evaluation: Complete Guide
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.
LLM Product Manager Workflows: A Complete Guide to AI Quality
A practical guide to LLM product manager workflows, built around the two things PMs can finally do without waiting on engineering: build on the AI product by editing prompts, running evals, and comparing variants, and monitor quality with dashboards, signals, and shareable evidence.
The Complete Guide to LLM Experimentation: Compare Prompts, Models, and Agents
A practical guide to running LLM experiments across prompts, models, tools, datasets, metrics, production A/B tests, and human-in-the-loop feedback loops.