Eval Harness
An eval harness is automated infrastructure that scores AI outputs against business, safety, and policy criteria on every release and in production.
An eval harness is automated infrastructure that scores AI outputs against business, safety, and policy criteria on every release and in production.
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Continuous eval
Harnesses feed model risk evidence and trigger alerts when quality drifts. FDEEs maintain harnesses as policies and products change.
Eval Harness is essential for governed production AI — not optional for regulated deployments
Pilots that skip this discipline typically stall at proof-of-concept
Derisk360 implements through accelerators with embedded Forward Deployed Engineers
FDEE-led eval harnesses run before and after production deployment
Related resources
- Agent Evaluation
Agent Evaluation — enterprise AI deployment from Derisk360.
- AI Evaluation Framework
AI Evaluation Framework — practical enterprise AI deployment guide from Derisk360.
- AI Evaluation
What is AI Evaluation? AI evaluation measures quality, safety, and business outcomes of AI systems before and after deployment.
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Common questions about Eval Harness
- What is Eval Harness?
- An eval harness is automated testing infrastructure that scores AI outputs against business and safety criteria.
- Why does Eval Harness matter for enterprise AI deployment?
- Eval Harness reduces deployment risk and determines whether agents reach governed production in regulated environments. Without it, pilots stall and compliance teams block go-live.
- How does Eval Harness relate to the 4-Layer Intelligence Stack?
- Eval Harness maps to one or more layers — context, decisions, actions, or outcomes — in Derisk360's architecture for production agentic systems.
- How does Derisk360 implement Eval Harness?
- Through structured AI accelerators and embedded FDEs who implement eval harness in your VPC — with evaluation and managed operations built in from day one.
- Is this a software product I can licence?
- No. Derisk360 is a services firm. You engage for production outcomes through accelerators and implementations, not shelfware.