The Ultimate Guide to AI Agent Quality: 4 Pillars of Trust скачать в хорошем качестве

The Ultimate Guide to AI Agent Quality: 4 Pillars of Trust

Building autonomous AI agents is easy. Building agents you can actually trust? That’s the hard part. 🤖🔒 If you’re still testing your AI agents like traditional software—looking for a simple pass/fail—you are missing the full picture. In this video, we unpack why traditional QA breaks down with LLMs and introduce a new mental model for evaluating agentic workflows: The F1 Car Problem. 🏎️💨 We break down the "Trajectory is Truth" philosophy and the exact framework you need to measure quality effectively. In this video, we cover: ⏱️ The Paradox of Agent Autonomy 🤯 The "F1 Car" Problem (Why old testing fails) 🏁 Why the Trajectory is the Truth 🗺️ The 4 Pillars of Quality: Effectiveness & Efficiency 🎯⚡ The 4 Pillars: Robustness & Safety 🛡️⚖️ The "Outside-In" Evaluation Playbook 📖 Observability: Logs, Traces, and Metrics 📊 The Agent Quality Flywheel ⚙️ Key Concepts: 📦 Black Box vs. Glass Box Testing: Knowing what happened vs. why it happened. 🏛️ The 4 Pillars: Effectiveness, Efficiency, Robustness, and Safety/Alignment. 👨‍⚖️ LLM-as-a-Judge: How to use pairwise comparison to scale your evaluations. 👣 Observability: Why logs are just facts, but traces are the story. If you are an AI Engineer, Product Manager, or Developer building with LangChain, AutoGen, or custom agent architectures, this framework will help you move from "it works on my machine" to production-grade reliability. 🚀 #AIAgents #LLM #MachineLearning #AIQuality #Observability #ArtificialIntelligence #TechEducation