Eval directory
Evals for Modal
7 evaluation packs covering adversarial robustness, safety gates, workflow quality, and operator-level checks for Modal AI products.
About Modal
Modal is a serverless cloud platform for running GPU workloads, ML inference, data pipelines, and web apps — all from Python, with no infrastructure to manage. Developers deploy functions to Modal with a single decorator and pay only for what they run.
Available eval packs for Modal
7 packs ready to run.
Distributed Dict Queue
Modal evals — Distributed Dict & Queue (relift v3)
Function Runtime Cold Start
Modal evals — Function Runtime & Cold Start (relift v3)
Sandboxes Code Execution
Modal evals — Sandboxes & Code Execution (relift v3)
Scheduled Jobs Cron
Modal evals — Scheduled Jobs & Cron (relift v3)
Secrets Billing Observability
Modal evals — Secrets, Billing & Observability (relift v3)
Volumes Image Build Cache
Modal evals — Volumes & Image Build Cache (relift v3)
Web Endpoints Request Auth
Modal evals — Web Endpoints & Request Auth (relift v3)
Why eval Modal AI
Modal's AI features ship behind brand promises about accuracy, safety, and reliability. Buyers and integrators need to know those promises hold up under adversarial prompts, edge-case workflows, and the long tail of real customer inputs — not just the demo path.
The Corsac eval library for Modal measures four dimensions teams care about most when deploying ai platform agents:
- Adversarial robustness — does the agent resist prompt injection, jailbreaks, and social-engineering attempts?
- Workflow quality— does it complete the task buyers were shown in the demo, on inputs that don't look like the demo?
- Safety gates — does it escalate or refuse when it should, and only then?
- Operator quality — does it preserve analyst trust by surfacing the right context at the right time?
Every eval pack above is hand-authored against Modal's public product surface and runnable in Corsac with your own data.