Enterprise AI Agent Evaluation: Testing to Production

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 a production-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 use LLM-as-a-Judge responsibly with calibration and reliability controls.

We then go deeper into evaluating the major components of modern agentic systems, including RAG and groundedness, tool use and function calling, agent workflows, and multi-agent systems.

You will also learn how to evaluate safety 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 through evaluation 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 understand how evaluation becomes an architectural capability for operating AI agents safely and reliably in production.

This course is designed for AI 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.