Presentation: From AI Agent Demo to Production: Automated Testing and Evaluation
Why 80% of AI agents never leave the demo phase — and how Columbia and Arklex solved it with synthetic testing.
Why it matters
A practical blueprint for moving agents from proof-of-concept to production reliability. Zhou Yu's simulation-driven testing approach (synthetic personas, trajectory entropy, automated CI/CD) addresses the compliance and edge-case bottlenecks that stall agent deployments — directly actionable for teams piloting multi-turn systems.
The key facts
8 to knowSimulation-driven testing framework for multi-turn agents
Synthetic user personas and trajectory entropy as evaluation methods
Automated CI/CD pipelines for agent validation
Edge-case detection before deployment
Self-learning workflow scaling in production
Columbia and Arklex AI case studies
Compliance and reliability focus
Demo-to-production bottleneck identified
Go to the source
InfoQ AI/MLinfoq.com
Publisher excerpt: Zhou Yu discusses why AI agents stall in demo phase and shares how simulation-driven testing solves compliance and reliability bottlenecks. Learn how Columbia and Arklex AI use synthetic user personas, trajectory entropy, and automated CI/CD pipelines to evaluate multi-turn agents, catch edge cases…