Enterprise AI Agent Evaluation Testing to Production
Published 9/2026
Created by Mohammad Naushad
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All Levels | Genre: eLearning | Language: English | Duration: 19 Lectures ( 2h 3m ) | Size: 648.5 MB
Evaluate AI agents with datasets, LLM judges, RAG, tool testing, safety, observability, CI/CD and production monitoring
What you'll learn
Design production-grade evaluation architectures for enterprise AI agents beyond simple response accuracy Build evaluation datasets, test cases, metrics, scoring models, thresholds, and hard gates for AI agent behavior Apply LLM-as-a-Judge and evaluate RAG, tool execution, workflows, and multi-agent systems effectively Evaluate AI agents for safety, policy compliance, adversarial behavior, reliability, performance, and cost Design observability and evaluation traces to understand why an AI agent succeeded or failed Integrate agent evaluation with CI/CD, regression testing, production monitoring, and continuous improvement.Requirements
Basic understanding of Generative AI, LLMs, and AI agents is helpful No advanced AI, machine learning, or mathematics knowledge is required Familiarity with enterprise applications, APIs, or solution architecture is useful but not mandatoryDescription
AI agents can produce impressive demos. But how do you know they are actually ready for production?
Enterprise AI agents are fundamentally different from traditional software. A response can look correct while the underlying agent selected the wrong tool, retrieved unreliable information, violated a policy, followed an incorrect workflow, or created unacceptable latency and cost.
This course teaches you how to design aproduction-grade evaluation strategy for enterprise AI agents - moving beyond simple response accuracy toward systematic evaluation of the complete agent behavior.
You will learn how to build evaluation datasets and test cases, define meaningful metrics and scoring models, establish thresholds and hard gates, and useLLM-as-a-Judge responsibly with calibration and reliability controls.
We then go deeper into evaluating the major components of modern agentic systems, includingRAG and groundedness, tool use and function calling, agent workflows, and multi-agent systems.
You will also learn how to evaluatesafety and policy compliance, adversarial behavior, reliability, performance and cost, and how production monitoring and online evaluation complement offline testing.
Finally, we connect these capabilities into an enterprise operating model throughevaluation observability and traces, CI/CD regression evaluation, and enterprise evaluation architecture.
Throughout the course, the emphasis is not simply on individual metrics or evaluation tools. The goal is to help you understandhow evaluation becomes an architectural capability for operating AI agents safely and reliably in production.
This course is designed forAI architects, solution architects, enterprise architects, AI engineers, technical leaders, developers and technology professionals who want to move AI agents from experimentation to dependable enterprise systems.
By the end of the course, you will be able to reason about a complete agent evaluation architecture - from test datasets and evaluator design to production monitoring and continuous validation.
Who this course is for
Enterprise and Solution Architects designing production-grade Agentic AI solutions AI/ML Architects, GenAI Engineers, and Agentic AI Developers moving AI agents from prototype to production Platform, DevOps, MLOps, and LLMOps professionals responsible for AI reliability, observability, and governance Technical leaders and consultants who need to design, review, or govern enterprise AI agent solutionsHomepage
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