Alex Dudov
⬤ Bytedance Research has rolled out VeAgentBench, a new benchmark for testing how well AI agents handle real-world scenarios. The dataset packs 145 open-ended questions—484 total items—covering everything from legal aid and financial analysis to educational tutoring and personal assistance. The project comes with a complete directory structure showing domain-specific datasets, agent implementations, utility functions, and a knowledge base that mirrors how agents would actually work in practice.
⬤ VeAgentBench ships with ready-to-run agents powered by veADK, Bytedance's full-stack agent framework. The setup includes CSV datasets for each domain, matching Python agent files, dataset-loading tools in a utils directory, and reference materials in a knowledge folder. The benchmark tests core capabilities that matter for advanced agent systems: structured tool use, retrieval-augmented generation, memory handling, and multi-step reasoning. These features reflect what's needed when deploying agents across multiple real-world domains.
⬤ The 145 open questions demand domain-specific thinking and smooth workflow execution. Financial prompts connect to structured data, educational tasks need adaptive responses, legal items test procedural knowledge, and personal-assistant scenarios check contextual understanding. Released under CC BY-NC 4.0 license for non-commercial use, the benchmark is hosted on ModelScope with full agent implementations, giving developers and researchers a transparent, reproducible way to evaluate real-world agent performance.
⬤ VeAgentBench signals a shift toward benchmarks that measure practical, tool-driven agent capabilities instead of synthetic prompt tests. As agent systems lean more heavily on retrieval pipelines, memory, and tool coordination, structured benchmarks like this one offer a clearer picture of what today's agents can actually handle, where they fall short, and what needs work in the rapidly expanding agentic AI space.
Alex Dudov
