AFS: Towards an Agent-Native File System

AI agents increasingly operate in file-rich workspaces that extend beyond a single context window. However, conventional file system interfaces are designed for humans, whose way of perceiving and navigating files differs fundamentally from that of LLMs. This mismatch severely limits the efficiency and speed of agent file access. We introduce AFS, an agent-native file system with three complementary designs. First, it uses offline preprocessing to organize files into identifiable source spans and builds reusable per-file key–value (KV) caches. Second, several parallel File Agents use continuous batching to inspect multiple files efficiently through shared model forward passes. Third, AFS decouples file inspection from reasoning, allowing File Agents to continue searching while the Main Agent reasons and to deliver selected original evidence, rather than entire files, to the Main Agent’s context. Together, these designs broaden file inspection without repeatedly requiring a new Main Agent decision or loading the full contents of every inspected file. Across Qwen3.5-9B and Qwen3.6-27B on LoCoMo, HotpotQA, and 2WikiMultiHopQA, AFS achieves the highest mean F1 score while admitting 77–88% less unique source text into the Main Agent’s context than Dynamic RAG. On 2WikiMultiHopQA with the 9B model, AFS improves F1 from 0.661 to 0.795 over Dynamic RAG while reducing mean online latency by 33.3%.