Evals

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.

TL;DR AI agent evaluation has become a production requirement in 2026 as organizations deploy increasingly autonomous agents. This guide examines five platforms for evaluating AI agents: Maxim AI leads the pack with its end-to-end approach combining simulation, experimentation, and observability specifically built for multi-agent systems. LangSmith offers deep LangChain

As organizations deploy AI agents and LLM-powered applications into production, the need for evaluation frameworks that hold up under load has become critical. Without proper evaluation tools, teams struggle to measure quality improvements, catch regressions before they ship, and maintain reliable performance at scale. The right evaluation platform enables teams

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

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

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