Building Reliable AI Agents with a Continuous Evaluation Loop

Developing reliable AI agents starts with pre-production testing. Using a retail checkout agent as an example, this video shows how to compare a candidate agent against a baseline using a golden dataset. A testing harness runs the cases, while human evaluators, programmatic checks, and LLM judges assess behavior against a defined rubric. The results guide improvements and help determine when the agent is ready for production. Once deployed, continuous evaluations at scale identify unexpected behavior and critical failures in production scenarios. An out-of-stock product recommendation illustrates how human review can turn a production failure into a new test case. Adding these cases to the next round of experiments closes the evaluation loop, connecting pre-production testing with production insights to improve agent reliability over time. What You'll Learn: – The continuous evaluation loop: Connect pre-production testing, production evaluations, and release and review processes. – Baseline comparisons and golden datasets: Test a candidate agent against a baseline using a defined set of test cases. – Rubrics and evaluators: Assess behavior with human evaluators, programmatic checks, and LLM judges for semantic criteria. – Release decisions: Use experiment results and iterative improvements to determine when a candidate is ready for production. – Production evaluations at scale: Identify outliers and critical failures in scenarios the agent was not tested for. – Human review and test-bank updates: Turn suitable production failures into test cases for the next cycle of experiments. Timestamps: 0:00 Building Reliable AI Agents with Continuous Evaluation 0:15 Pre-Production Testing: Baselines, Golden Datasets, & Rubrics 0:37 Human, Programmatic, & LLM Evaluators 0:53 Using Experiment Results to Make Release Decisions 1:03 Continuous Production Evaluations at Scale 1:23 Grounding Failures, Human Review, & New Test Cases 1:47 Closing the Evaluation Loop