Evals

TL;DR Evaluating AI agents in 2025 requires platforms that can simulate multi-turn interactions, check whether agents make correct tool calls, and test how well they handle and recover from errors during a task. Leading platforms such as Maxim AI, LangSmith, Langfuse, Arize Phoenix, Comet, Confident AI, and RAGAS differ

TL;DR Agentic AI systems must be evaluated beyond static benchmarks. Effective assessment spans three layers: system efficiency (latency, tokens, tool usage), session-level outcomes (task success, step completion, trajectory quality, self-aware failures), and node-level precision (tool selection, error rate, tool call accuracy, plan evaluation, step utility). This structure quantifies planning,

Evaluating large language models (LLMs), retrieval-augmented generation (RAG), and multimodal agents is no longer optional, it is essential for ensuring AI quality. AI evals platforms give engineering and product teams a common framework to quantify quality, trace decisions, detect hallucinations, and compare changes before they reach production. This guide explains

Evaluating AI agents is essential for reliability. Real-world interactions expose non-determinism, model updates, and hallucinations, which degrade trust without rigorous checks. A structured evaluation approach (pre-release and in production) helps quantify quality, prevent regressions, and align systems to human preference. Maxim AI provides unified capabilities across offline testing, online evaluations,

TL;DR * RAG systems combine retrieval and generation; evaluation must assess both components. * Retrieval quality hinges on recall, precision, relevance, and timeliness of sources. * Generation quality requires grounding, faithfulness, clarity, and low hallucination rates. * Judge reliability improves with mixed methods: LLM-as-a-judge, programmatic checks, and human review. * Use Maxim’s offline