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Decagon

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Evals for Decagon

4 evaluation packs covering adversarial robustness, safety gates, workflow quality, and operator-level checks for Decagon AI products.

Medical & Clinical AI
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About Decagon

Decagon builds AI customer support agents that understand full conversation context, integrate with existing helpdesks, and resolve tickets end-to-end without human intervention. Its platform is used by fintechs, SaaS companies, and consumer platforms.

Employees

~80

Industry

Customer Support AI

Headquarters

San Francisco, CA

Website

decagon.ai

Available eval packs for Decagon

4 packs ready to run.

Why eval Decagon AI

Decagon'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 Decagon measures four dimensions teams care about most when deploying medical & clinical ai 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 Decagon's public product surface and runnable in Corsac with your own data.