As generative AI becomes increasingly integrated into software systems, a fundamental challenge has emerged: how to effectively manage the vast amounts of external knowledge, memory, and tools these systems need to function reliably. Prompt engineering and retrieval-augmented generation (RAG) have proven insufficient, creating transient artifacts that lack traceability and accountability. A new research paper from arXiv:2512.05470 introduces an innovative solution inspired by a decades-old computing philosophy.

The paper, titled "Everything is Context: Agentic File System Abstraction for Context Engineering," proposes a file-system abstraction that draws directly from the Unix principle that "everything is a file." This approach offers a persistent, governed infrastructure for managing heterogeneous context artifacts through uniform mounting, metadata, and access control.

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.

The researchers, led by Xiwei Xu from what appears to be a collaborative academic effort, have implemented this architecture within the open-source AIGNE framework. Their solution creates a verifiable context-engineering pipeline comprising three key components:

  1. Context Constructor: Assembles relevant information from various sources
  2. Context Loader: Manages how this information is delivered to the AI system
  3. Context Evaluator: Validates the context under token constraints

This structured approach addresses a critical gap in current GenAI implementations. Rather than treating context as an ephemeral component of prompts, the file system abstraction provides permanence and governance, essential for building trustworthy AI systems that can be audited and refined over time.

The implementation demonstrates practical applications through two exemplars:

  • An AI agent with persistent memory capabilities
  • An MCP-based GitHub assistant that can leverage context-aware tool integration

These examples illustrate how the architecture can be operationalized in both developer and industrial settings, supporting verifiable, maintainable, and industry-ready GenAI systems.

The researchers emphasize that as GenAI becomes an active collaborator in decision support, humans must play a central role as curators, verifiers, and co-reasoners. The proposed architecture establishes a reusable foundation for this human-centered AI co-work.

This approach represents a significant shift in how we think about building AI systems. By borrowing a concept from operating system design—treating all context as files that can be managed, versioned, and secured—the researchers provide a blueprint for more robust and accountable AI implementations.

The open-source nature of the AIGNE framework suggests this could rapidly influence how developers integrate GenAI capabilities into their applications, potentially setting new standards for context management in AI-powered software.