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.

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

TL;DR Voice agents introduce unique debugging challenges that traditional LLM monitoring tools can't handle. Speech-to-text errors, latency issues, conversational flow breakdowns, and audio quality problems require specialized observability. This guide compares the top 5 platforms for debugging voice agents: Maxim AI (comprehensive simulation and evaluation for voice

TL;DR Shipping reliable LLM applications requires systematic evaluation beyond manual testing. Maxim AI provides end-to-end evaluation with simulation, node-level metrics, and production feedback loops. Langfuse offers open-source evaluation with prompt management. LangSmith delivers LangChain-native testing with datasets. TruLens specializes in feedback-driven improvement. Deepchecks brings MLOps validation to LLM workflows.