Kamya Shah

Human annotation remains the gold standard for training and evaluating AI systems, yet managing annotators effectively presents significant challenges for AI teams. As enterprises scale their AI applications, establishing robust annotation workflows becomes critical to maintaining model quality and reliability. This guide explores evidence-based practices for managing human annotation in

TL;DR Building robust evaluation datasets is critical for measuring and improving AI agent performance. This guide covers the complete lifecycle of dataset creation—from defining objectives and sourcing data to curating multi-modal examples and implementing continuous improvement workflows. Quality evaluation datasets enable teams to run meaningful evaluations, catch regressions

TL;DR Testing and evaluating AI agents in production requires a systematic approach that combines pre-deployment validation, continuous monitoring, and iterative improvement. This guide covers essential strategies including simulation-based testing, multi-layered evaluation frameworks, real-time observability practices, and data curation techniques. Organizations can achieve reliable AI agent deployments by implementing comprehensive