Evals

As AI agents are becoming more mainstream and have started powering mission-critical business workflows, evaluating your AI Agents has become imperative. In 2025, the landscape of AI evaluation tools has matured rapidly, offering teams not just basic benchmarking but comprehensive observability, simulation, and evaluations to test their AI applications end-to-end.

TL;DR Advanced AI models currently solve less than 2% of problems in FrontierMath, a benchmark designed by expert mathematicians to test research-level mathematical reasoning. This represents a significant gap between current AI capabilities and human-level mathematical expertise. As AI systems approach this milestone, organizations must prepare with robust evaluation

TL;DR Production-ready LLM applications require comprehensive evaluation frameworks combining automated assessments, human feedback, and continuous monitoring. Key components include clear evaluation objectives, appropriate metrics across performance and safety dimensions, multi-stage testing pipelines, and robust data management. This structured approach enables teams to identify issues early, optimize agent behavior systematically,

TL;DR Human-in-the-loop evaluation fills critical gaps that automated evaluators miss in agentic AI systems. This guide explains how to integrate Human-in-the-loop with machine evaluators, distributed tracing, and production observability. You'll learn when to route interactions to humans, how to structure effective rubrics, and how to convert feedback

A/B testing has evolved from a simple website optimization technique to a critical methodology for evaluating and improving AI agent performance. As enterprises deploy increasingly sophisticated agentic AI systems, traditional testing approaches often fall short. AI agents are transforming A/B testing from a blunt instrument into a precision

Human feedback has become essential for building production-ready AI systems. While automated evals provide speed and consistency, they cannot capture the nuanced quality requirements that define real-world AI performance. OpenAI's research on InstructGPT demonstrated that a 1.3B parameter model trained with human feedback outperformed a 175B parameter