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TL;DR Agentic AI systems require evaluation beyond single-shot benchmarks. Use a three-layer framework: System Efficiency (latency, tokens, tool calls), Session-Level Outcomes (task success, trajectory quality), and Node-Level Precision (tool selection, step utility). Combine automated evaluators like LLM-as-a-Judge with human review. Operationalize evaluation from offline simulation to online production monitoring

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

As AI agents are becoming more mainstream and have started powering mission-critical business workflows, evaluating your AI Agents has become imperative. In 2025, the landscape of AI evaluation tools has matured rapidly, offering teams not just basic benchmarking but comprehensive observability, simulation, and evaluations to test their AI applications end-to-end.

TL;DR Advanced AI models currently solve less than 2% of problems in FrontierMath, a benchmark designed by expert mathematicians to test research-level mathematical reasoning. This represents a significant gap between current AI capabilities and human-level mathematical expertise. As AI systems approach this milestone, organizations must prepare with robust evaluation

TL;DR Production-ready LLM applications require comprehensive evaluation frameworks combining automated assessments, human feedback, and continuous monitoring. Key components include clear evaluation objectives, appropriate metrics across performance and safety dimensions, multi-stage testing pipelines, and robust data management. This structured approach enables teams to identify issues early, optimize agent behavior systematically,