Agentic AI is transforming how models interact with the world by proactively making decisions and executing complex tasks with minimal guidance, necessitating advanced training techniques and high-quality data. Labelbox collaborates with AI labs to develop data infrastructure that supports the creation of agentic systems, which require detailed feedback, verifiable outcomes, and scalable evaluation pipelines. The company has worked on projects that involve simulating complex tool use, verifying structured reasoning, and benchmarking multi-turn instruction-following, demonstrating the capacity of AI to plan, adapt, and respond autonomously in real-world contexts. These initiatives include developing environments for multi-step API interactions, creating benchmarks for planning tasks with constraints, and evaluating models' ability to follow evolving instructions. Through these projects, Labelbox aims to benchmark and enhance the performance of agentic AI systems, which are crucial for AI assistants and tool-using agents, by providing flexible infrastructure for training and evaluation.