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.
How complete this published benchmark library is across datasets, metrics, rubrics, use-case maps, and pack context. This is library coverage, not an agent performance score.
Test datasets
7/7 packs
Scoring metrics
0/7 packs
Judge rubrics
7/7 packs
Use-case maps
0/7 packs
Pack context
7/7 packs
Available eval packs for Modal
7 packs ready to run.
Distributed Dict Queue
Evaluates Modal's Distributed Dict & Queue across 10 scenario-based test cases, each graded against an expected-behavior rubric by an LLM judge, from Corsac's AI infrastructure eval coverage.
Function Runtime Cold Start
Evaluates Modal's Function Runtime & Cold Start across 11 scenario-based test cases, each graded against an expected-behavior rubric by an LLM judge, from Corsac's AI infrastructure eval coverage.
Sandboxes Code Execution
Evaluates Modal's Sandboxes & Code Execution across 10 scenario-based test cases, each graded against an expected-behavior rubric by an LLM judge, from Corsac's AI infrastructure eval coverage.
Scheduled Jobs Cron
Evaluates Modal's Scheduled Jobs & Cron across 9 scenario-based test cases, each graded against an expected-behavior rubric by an LLM judge, from Corsac's AI infrastructure eval coverage.
Secrets Billing Observability
Evaluates Modal's Secrets, Billing & Observability across 11 scenario-based test cases, each graded against an expected-behavior rubric by an LLM judge, from Corsac's AI infrastructure eval coverage.
Volumes Image Build Cache
Evaluates Modal's Volumes & Image Build Cache across 10 scenario-based test cases, each graded against an expected-behavior rubric by an LLM judge, from Corsac's AI infrastructure eval coverage.
Web Endpoints Request Auth
Evaluates Modal's Web Endpoints & Request Auth across 10 scenario-based test cases, each graded against an expected-behavior rubric by an LLM judge, from Corsac's AI infrastructure eval coverage.
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.