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Large Language Models (LLMs) power everything from chatbots to code generators, but their true value in production environments hinges on rigorous evaluation using detailed metrics. This guide breaks down key metrics, benchmarks, and best practices for assessing LLM performance, drawing from industry-leading research and tools to help you deploy reliable AI systems.[1][2] Why LLM Evaluation Matters in Production In production, LLMs face real-world challenges like diverse inputs, latency constraints, and ethical risks. Traditional metrics like perplexity fall short; instead, use a multi-faceted approach combining automated scores, human judgments, and domain-specific benchmarks to measure accuracy, reliability, and efficiency.[1][4] ...

Introduction In the rapidly evolving world of artificial intelligence, the term “agentic” has emerged as a buzzword describing systems that go beyond mere response generation to exhibit true autonomy and initiative. An AI agent is “agentic” when it can independently perceive its environment, reason about goals, plan actions, execute them, and adapt based on feedback—all with minimal human intervention.[1][2][3] This capability marks a shift from reactive tools like traditional generative AI to proactive entities capable of handling complex, real-world tasks.[4][10] ...

Table of Contents What Are Guardrails? Types of Guardrails Common Applications Guardrail Components and Standards Installation and Maintenance Choosing the Right Guardrail System Resources What Are Guardrails? Guardrails are longitudinal, roadside barrier systems designed to prevent errant vehicles from impacting roadside obstacles and to protect workers from falls and collisions. These essential safety structures serve as semi-flexible barriers that move or bend when hit, deflecting impacts up to 5 feet while maintaining structural integrity. Beyond roadways, guardrails are critical safety components in industrial facilities, construction sites, warehouses, and elevated work platforms. ...

Introduction “AI agents” are everywhere in current discourse: customer support agents, coding agents, research agents, planning agents. But the term is often used loosely, sometimes referring to: A single large language model (LLM) call A script that calls a model and then an API A complex system that plans, acts, remembers, and adapts over time To design, evaluate, or improve AI agents, you need a clear mental model of what an agent actually is and how its parts work together. ...

Introduction The promise of large language models (LLMs) is compelling: intelligent agents that can handle customer interactions, provide guidance, and automate complex tasks. Yet in practice, developers face a critical challenge that no amount of prompt engineering can fully solve. An AI agent that performs flawlessly in testing often fails spectacularly in production—ignoring business rules, hallucinating information, and delivering inconsistent responses that damage brand reputation and customer trust.[3] This gap between prototype and production is where Parlant enters the picture. Built by Emcie, a startup founded by Yam Marcovitz and staffed by engineers and NLP researchers from Microsoft, Check Point, and the Weizmann Institute of Science, Parlant is an open-source framework that fundamentally rethinks how developers build conversational AI agents.[3] Rather than fighting with prompts, Parlant teaches agents how to behave through structured, programmable guidelines, journeys, and guardrails—making it possible to deploy agents at scale without sacrificing control or compliance.[3] ...