Evals

TL;DR Choosing the right LLM evaluation platform is critical for shipping reliable AI agents in 2026. This comprehensive comparison examines the top 5 platforms: Maxim AI leads with end-to-end simulation, evaluation, and observability; Braintrust offers strong experiment tracking; LangSmith provides deep LangChain integration; Arize excels in ML monitoring; and

TL;DR: Evaluating RAG systems requires specialized platforms that measure both retrieval quality and generation accuracy. This guide covers the five leading RAG evaluation platforms in 2026: Maxim AI (comprehensive platform with simulation, evaluation, and observability), LangSmith (LangChain-focused tracing), Arize Phoenix (open-source observability), Ragas (reference-free evaluation framework), and DeepEval (pytest-style

TL;DR RAG systems fail silently when retrieval selects irrelevant documents, generation hallucinates despite good context, or quality degrades undetected. Five leading platforms address these challenges: Maxim AI integrates evaluation with simulation, experimentation, and observability for complete lifecycle management. Braintrust provides enterprise-grade evaluation with production feedback loops. Deepchecks delivers MLOps

TLDR AI agents are reshaping enterprise workflows, but evaluating their performance remains a critical challenge. This guide examines five leading platforms for agent evaluation in 2026: Maxim AI, LangSmith, Arize, Langfuse, and Galileo. Each platform offers distinct approaches to measuring agent reliability, cost efficiency, and output quality. Maxim AI leads

Pre-built evaluation metrics cover common quality dimensions like accuracy, relevance, and coherence. However, production AI applications require validation against domain-specific business rules, compliance requirements, and proprietary quality standards that generic evaluators cannot assess. Custom evaluators enable teams to enforce these specialized quality checks across AI agent workflows, ensuring applications meet

AI agents have evolved beyond simple question-answer systems into complex, multi-step entities that plan, reason, retrieve information, and execute tools across dynamic conversations. This evolution introduces significant evaluation challenges. Unlike traditional machine learning models with static inputs and outputs, AI agents operate in conversational contexts where performance depends on maintaining