Jason Liang

One of the biggest barriers enterprises face in scaling generative and agentic AI out of production is ensuring accuracy, reliability, and trustworthiness at every stage of the lifecycle. Proof-of-concepts often stumble when confronted with real-world complexity, where precision and quality can make or break adoption. This session explores why human review remains critical not just in data labelling, but in training, evaluating, and continuously monitoring AI agents for enterprise use.

• The role of human-in-the-loop systems in preventing errors, bias, and drift.
• Frameworks for measuring accuracy, consistency, and alignment throughout the AI lifecycle.
• Best practices for training, reviewing, and evaluating AI agents at scale.
• Strategies to overcome the POC-to-production gap and operationalize with confidence.
• How ongoing monitoring ensures enterprise-grade trust, safety, and performance

Session Reserved for SuperAnnotate