Title: Agent Data Protocol
Primary area: datasets and benchmarks
Keywords: agent, training, data

Abstract: Public research results on large-scale supervised finetuning of AI agents remain relatively rare, since the collection of agent training data presents unique challenges. In this work, we argue that the bottleneck is not a lack of underlying data sources, but that a large variety of data is fragmented across heterogeneous formats, tools, and interfaces. To this end, we introduce the agent data protocol (ADP), a light-weight representation language that serves as an ''interlingua'' between agent datasets in diverse formats and unified agent training pipelines downstream. The design of ADP is expressive enough to capture a large variety of tasks, including API/tool use, browsing, coding, software engineering, and general agentic workflows, while remaining simple to parse and train on without engineering at a per-dataset level. In experiments, we unified a broad collection of 13 existing agent training datasets into ADP format, and converted the standardized ADP data into training-ready formats for multiple agent frameworks. We performed SFT on these data, and demonstrated an average performance gains of 10\% over corresponding base models, and of as much as 8\% over the existing model-size-matched SOTA on standard coding, browsing, tool use, and research benchmarks. All code and data will be released open source, in the hope that ADP could help lower the barrier to standardized, scalable, and reproducible agent training.