How are you regression-testing agent workflows before users find the failures?

Curious how people here are testing AI agent workflows (Claude [Code], Codex, Cursor, etc) once they become more than a prompt. I mean the layer around the model: repo instructions, skills, MCP/tool setup, memory, hooks, guardrails, and the expected sequence of steps the agent should follow. The failure mode I keep seeing: one great transcript makes a workflow look “done,” then a later run fails because the skill didn’t activate, the wrong tool got used, a source got skipped, or the final artifact looked right for the wrong reasons. Are you testing only the final output, or the agent trajectory too? For example: - did the intended skill/rule activate? - did the agent call the right tools? - did it follow the expected workflow? - can you compare against a known-good baseline? Disclosure: I work on Runme (Open Source), and I’ve been thinking about this while building eval support there. The pattern I’m exploring is basically repo-local regression tests for deploying workflows into AI agent harnesses: record the task, run the agent, score artifact + trajectory, compare against baseline. Would love to hear how others are handling this. Heads up, this is not for BYO agents via SDKs or LLM model loops.