As AI agents gain prominence in 2025, Labelbox addresses the significant challenge of refining training and evaluation data for these agents through its Multimodal Chat Editor, which allows users to create, edit, and annotate agent trajectories. These trajectories consist of sequences of reasoning steps, tool calls, and observations that help agents achieve their goals. The Labelbox platform facilitates two essential tasks in agent development: training and evaluation. During training, human labelers can identify and rectify issues in agent trajectories, enhancing the agent's performance through prompt optimization and model fine-tuning. An example given is the development of a research agent using the DSPy package and the ReAct framework, where the agent's trajectory is refined to produce a structured report. For agent evaluation, Labelbox offers customizable classification features to assess agent performance on both a global and granular level, supporting the development and production phases. By improving agent trajectories, Labelbox aims to streamline the creation of effective and reliable AI models, emphasizing the importance of human feedback in this iterative process.