Evals

LLMs are non-deterministic by nature. The same prompt can produce different outputs across runs, and subtle changes in retrieval pipelines, model versions, or prompt templates can quietly degrade quality without triggering traditional error alerts. As AI agents move from prototypes to production, LLM evaluation platforms have become foundational infrastructure for

As AI agents move into production, evaluation is no longer optional. According to LangChain's 2026 State of AI Agents report, 57% of organizations now have agents in production, with quality cited as the top barrier to deployment by 32% of respondents. Unlike traditional software, agents are non-deterministic — the

AI agents now handle customer support inquiries, automate financial workflows, and orchestrate complex enterprise operations. According to LangChain's 2026 State of AI Agents report, 57% of organizations have agents in production, with quality cited as the top barrier to deployment by 32% of respondents. Unlike traditional software where

RAG systems now power the majority of production AI applications, from customer support agents to enterprise knowledge bases. Yet evaluating these systems remains uniquely challenging. Unlike standard LLM applications, RAG pipelines introduce dual failure points: retrieval can miss relevant documents, and generation can hallucinate or ignore context entirely. Traditional evaluation

TL;DR: AI agents are moving from prototypes to production, but their non-deterministic, multi-step nature demands specialized evaluation infrastructure. This guide covers five leading evals platforms in 2026: Maxim AI for end-to-end simulation, evaluation, and observability; Langfuse for open-source tracing; Arize AI for enterprise ML and LLM monitoring; LangSmith for

TL;DR As AI agents move from prototypes to production, evaluation platforms have become essential infrastructure. This article covers the top 5 platforms for ensuring AI quality in 2026: Maxim AI for end-to-end simulation, evaluation, and observability; Arize AI for enterprise ML monitoring with LLM support; LangSmith for LangChain-native debugging;