RainForest has released "Senda-Argus Hooks," a log collection library for AI Agents, on GitHub as a technical preview of "Senda-Argus," a purely domestic Security for AI platform that audits the judgment, reasoning, and execution of AI Agents. Some technologies related to Senda-Argus, concerning the collection of AI Agent execution traces, reconstruction of judgment processes, and correlation analysis of Runtime Audit Events, are currently pending patent applications in Japan. GitHub: https://github.com/rainforest-tokyo/senda_argus_hooks The utilization of generative AI is evolving from simple chat usage to referencing internal data via RAG, integrating with external tools via MCP, and autonomous judgment and execution by AI Agents. Meanwhile, in corporate security operations, mechanisms to audit and verify "why an AI Agent chose a particular tool and what it actually executed" are becoming crucial. Senda-Argus Hooks is a Collector that records and analyzes the entire flow of an AI Agent selecting tools using LLMs, referencing RAG, and calling external tools like MCP, step by step. By acquiring the task_summary, reason_summary, and selected_tool output by the LLM and cross-referencing them with Agent decisions and actual MCP Tool Calls, it enables auditing of "what task the AI Agent identified, why it selected that tool, and which MCP Tool it actually called." Furthermore, data that may contain important internal information, such as RAG context, prompt content, and MCP results, is not saved by default. Instead, it primarily records HASHes and metadata like messages_hash, context_hashes, result_hash, and purpose_id. This aims for Privacy-Safe AI Agent auditing in corporate environments. Background: New Security Auditing Required in the AI Agent Era AI Agents can execute tasks more flexibly and autonomously than traditional applications by referencing RAG based on LLM judgments and calling external tools via MCP or APIs. However, the following challenges are becoming