Evals

Voice AI agents are quickly becoming the backbone of customer support, sales outreach, and automated workflows. But unlike text-based applications, voice introduces a unique layer of complexity. Latency above 800ms breaks conversational flow. Background noise and regional accents degrade transcription accuracy. Users interrupt, change their minds mid-sentence, and express frustration

TL;DR AI agent evaluation has become critical as autonomous systems move to production. This guide compares the five leading agent evaluation platforms in 2026: Maxim AI for comprehensive simulation, evaluation, and observability; Langfuse for open-source tracing; Arize for ML monitoring with agent support; LangSmith for LangChain-native debugging; and Galileo

TL;DR AI agents represent a fundamental shift from traditional LLM applications, requiring specialized evaluation frameworks that go beyond single-turn metrics. Effective agent evaluation combines system efficiency metrics (token usage, completion time, tool calls) with agent quality metrics (task success, trajectory analysis, tool correctness) across both session and node levels.

Retrieval-augmented generation (RAG) has become the foundation of modern AI applications, powering everything from customer support chatbots to internal knowledge bases. According to industry estimates, RAG systems now power approximately 60% of production AI applications. However, the complexity of RAG pipelines (combining retrieval, context assembly, and generation) makes evaluation critical

TL;DR Evaluating RAG (Retrieval-Augmented Generation) systems is critical for ensuring accurate, reliable AI responses. This guide covers five leading RAG evaluation platforms: Maxim AI, LangSmith, Arize Phoenix, RAGAS, and DeepEval. While all platforms offer RAG evaluation capabilities, they differ significantly in scope, ease of use, and production readiness. Maxim

As organizations increasingly deploy AI agents and LLM-powered applications into production, the need for robust evaluation frameworks has become critical. Without proper evaluation tools, teams struggle to measure quality improvements, identify regressions, and ensure reliable performance at scale. The right evaluation platform enables teams to ship AI applications faster while