Everything is Context: Agentic File System Abstraction for Context Engineering

• URL: http://arxiv.org/abs/2512.05470v1
• Date: Fri, 05 Dec 2025 06:56:45 GMT
• Title: Everything is Context: Agentic File System Abstraction for Context Engineering
• Authors: Xiwei Xu, Robert Mao, Quan Bai, Xuewu Gu, Yechao Li, Liming Zhu,
• Abstract summary: This paper proposes a file-system abstraction for context engineering.<n>The abstraction offers a persistent, governed infrastructure for managing heterogeneous context artefacts.<n>As GenAI becomes an active collaborator in decision support, humans play a central role as curators, verifiers, and co-reasoners.
• Score: 11.63011212134865
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Generative AI (GenAI) has reshaped software system design by introducing foundation models as pre-trained subsystems that redefine architectures and operations. The emerging challenge is no longer model fine-tuning but context engineering-how systems capture, structure, and govern external knowledge, memory, tools, and human input to enable trustworthy reasoning. Existing practices such as prompt engineering, retrieval-augmented generation (RAG), and tool integration remain fragmented, producing transient artefacts that limit traceability and accountability. This paper proposes a file-system abstraction for context engineering, inspired by the Unix notion that 'everything is a file'. The abstraction offers a persistent, governed infrastructure for managing heterogeneous context artefacts through uniform mounting, metadata, and access control. Implemented within the open-source AIGNE framework, the architecture realises a verifiable context-engineering pipeline, comprising the Context Constructor, Loader, and Evaluator, that assembles, delivers, and validates context under token constraints. As GenAI becomes an active collaborator in decision support, humans play a central role as curators, verifiers, and co-reasoners. The proposed architecture establishes a reusable foundation for accountable and human-centred AI co-work, demonstrated through two exemplars: an agent with memory and an MCP-based GitHub assistant. The implementation within the AIGNE framework demonstrates how the architecture can be operationalised in developer and industrial settings, supporting verifiable, maintainable, and industry-ready GenAI systems.

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