Automated Agent Logic Validation
Building AI agents is exciting—until you need to prove they actually work. When you're iterating on a System Prompt, updating Grounding Data, or wiring up new tools, the only way to know if things still behave correctly has been manual, one-off testing. Change the prompt logic, re-deploy, and hope for the best. That kind of guesswork doesn't scale, and it certainly doesn't inspire the confidence needed to put agents in front of real users.
We saw a clear need for a structured, repeatable way to validate agent behavior during development—something that lets you know, before anything reaches production, whether your changes improved things or broke them.
Introducing Evaluations in Agent Workbench
Evaluations let you automatically test your Agent Logic against a Golden Dataset—a collection of test cases with inputs, expected outputs, and expected tool calls that you define. You upload the dataset as JSON, select the Service Action that wraps your agent, and run an evaluation. The platform executes each test case, captures a full execution trace (inputs, tool calls, token usage, and final output), and then automatically scores every result using the built-in platform Judge. No configuration needed—every run gets a quality score and Pass/Fail for each test case out of the box.
This means you can change a prompt, swap a tool, or adjust grounding logic and immediately see how those changes affect accuracy and reliability across dozens of scenarios. The full execution trace gives you the visibility to pinpoint exactly where things went wrong—which tool was called, what arguments were passed, and what the agent actually produced. And because the Judge runs automatically on every evaluation, you get consistent, comparable feedback across runs without any extra setup.
Learn more about Automated Agent Logic Validation- Artificial Intelligence
