Evals

TL;DR Evaluating AI agents requires a rigorous, multi-dimensional approach that goes far beyond simple output checks. This blog explores the best practices, metrics, and frameworks for AI agent evaluation, drawing on industry standards and Maxim AI’s advanced solutions. We cover automated and human-in-the-loop evaluations, workflow tracing, scenario-based testing,

This article explains why evals are essential, what they should look like beyond leaderboard benchmarks, and how to build a practical evaluation program that improves product quality week after week. It also shows how to implement these ideas using Maxim AI, with specific workflows and resources to get you from

If your customers depend on your AI to be right, your retrieval augmented generation pipeline is either earning trust or eroding it on every query. The difference often comes down to what you measure and how quickly you act on it. This guide shows you how to build a rigorous,

AI evaluation has shifted from static correctness checks to dynamic, context-aware judgment. As applications evolve beyond single-turn prompts into complex agents, tool use, and multi-step workflows, teams need evaluation that mirrors how users actually experience AI. Enter “LLM as a Judge” — using a model to evaluate other models or agents.

TL;DR Enterprise AI evaluation must cover three layers end to end: experiment, evaluate, and observe. Choose a platform that unifies offline evals, agent simulations, and online evals in production, and integrates with your observability stack. Priorities for 2025 include OpenTelemetry compatibility, human-in-the-loop pipelines, dataset curation from production logs, and

AI quality comes down to two things: how well the agent performs across a whole session, and how solid each step inside that session is. Session metrics tell you if the agent hit the goal and kept it safe for the user. Node metrics dig into every action and decision