The Data Exploration Agent launched by NVIDIA's KGMON (NeMo Agent Toolkit) team has achieved a major breakthrough in data intelligent processing, topping the Multi-Step Reasoning Data Agent Benchmark (DABStep). Designed specifically to address the scarcity of quantitative structured data on the internet, this tool aims to simulate senior data scientists' workflows, enabling automated exploratory data analysis, table question answering, and predictive modeling. For tabular data requiring complex multi-step queries, traditional agents relying solely on web search often fail. Data Explorer employs a unique three-stage architecture: First, during the "learning phase," large models batch-process sample tasks to extract and encapsulate general-purpose function libraries (helper.py), consolidating scattered logic into reusable modular components while adhering to efficient principles like "write once, run anywhere." Second, in the "reasoning phase," lightweight fast models directly invoke pre-generated function libraries to handle new tasks without regenerating underlying logic, significantly reducing latency and token consumption. Finally, an offline reflection stage uses large models to conduct unsupervised audits and consistency analyses of past tasks, feeding insights back into system prompts to continuously improve reasoning accuracy without impacting online speed. Empirical results demonstrate remarkable performance at the DABStep benchmark. In handling challenging multi-step reasoning tasks, Data Explorer achieves an accuracy rate of up to 89.95%, far surpassing competitors using heavy-duty models such as Claude Code (66.93%) and Google AI (45.24%). Meanwhile, its single-task completion time averages just 20 seconds, with generated code length reduced to 1,870 characters—representing a 30-fold efficiency gain compared to conventional manual coding approaches. These findings validate that separating foundational knowledge construction from rapid inference enables lightweight models to outperform heavyweight counterparts in complex data analytics, establishing a new paradigm for data-intensive research. NVIDIA has now made these tools publicly accessible for developers building custom data exploration Agents.