Tabular data discovery aims to identify the tables relevant to a natural-language question from a large collection of heterogeneous data. Existing systems typically assume that a question is answered by a single table and rely on retrieval pipelines constructed through substantial offline preprocessing. These assumptions become restrictive when relevant information is distributed across multiple tables. In this paper, we present Octopus, a lightweight agentic framework for multi-table data discovery. Octopus follows a thin-harness design that combines lightweight offline semantic guidance with adaptive access to raw tabular data. At query time, an LLM agent dynamically orchestrates semantic column search, structured tabular operations, and general-purpose execution to locate and verify relevant tables based on intermediate observations. We further introduce a benchmark that explicitly evaluates multi-table data discovery, covering both settings where multiple tables independently answer a question and settings where multiple tables must be joined. Experimental results across four independent-discovery datasets and two join-based datasets show that Octopus achieves the highest macro-F1 on all six datasets. It also substantially reduces offline preprocessing overhead relative to Pneuma and online latency and API cost relative to Claude Code. When coupled with the same NL2SQL agent, Octopus yields substantially higher downstream question-answering F1 than Claude Code, showing that improved table discovery provides more complete evidence for downstream analytics. Code is available at https://github.com/wenzhilics/octopus.

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