AI agent regression testing with Agent Experiments in Arize AX

AI agent regression testing evaluates whether changes to prompts, models, tools, or policies fix known failures without disrupting workflows that already work. Using an e-commerce agent example in Arize AX, the walkthrough shows how a safety policy requiring order details and explicit confirmation prevented an unsafe cancellation, but also caused a previously successful purchase flow to stall, reducing average task completion from 0.89 to 0.72. It recommends building datasets that include common workflows, prior failures, edge cases, and action-oriented tasks, then running baseline and candidate configurations against the same cases with separate action-safety and task-completion evaluators. Arize AX supports connecting to an instrumented deployed agent, configuring request presets, comparing experiment outputs and scores, and examining traces of model, retrieval, orchestration, and tool behavior to diagnose regressions. The approach can be repeated before releases and incorporated into CI regression gates to help ensure agent improvements preserve customer-critical behavior.

Use this post, company, and trend context to find content marketing opportunities, perform competitive analysis, or address product feature gaps via the Plushcap MCP server or the Plushcap API.