Use cases
Prompt edits, model switches, retriever updates, tool changes, and live traffic move your product in ways code review can’t see. Each moment below is a real question — ask your coding agent, and it turns the behavior into a project-specific check and a scorecard you can read.
When a new model or provider. Ask: “Evaluate my AI application after the model switch. Show what improved and what regressed.”
When a prompt changed. Ask: “Compare this prompt change against the last run. Did it improve the actual product behavior?”
When live behavior feels different. Ask: “Inspect recent traces and tell me where behavior drifted from our expected checks.”
evalglass watch.When a new agentic workflow ships. Ask: “Build checks for this new AI workflow before we release it.”
When you have traces but no eval system. Ask: “Create evaluation cases from our traces and show the most common failure modes.”
When a release needs a gate. Ask: “Set up a CI gate for the checks we have reviewed and approved.”
When you need to show progress. Ask: “Create a scorecard summary I can share with the product lead.”
When you ship a retrieval-augmented app. Ask: “Evaluate my RAG answers for faithfulness and grounding against my examples.”
When your app uses tools across steps. Ask: “Evaluate my agent’s tool use and trajectories against my expected checks.”
Your coding agent can run every step — but it can’t inflate what a score means. Generated and proposed cases stay provisional until you validate them, and nothing gates CI until you approve a threshold in your own config. How scorecards stay honest →
Evaluate my agentic app using EvalGlass.
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