The evolving landscape of AI Agents is shifting focus from model fine-tuning to context engineering—the art and science of capturing, structuring, governing, and leveraging external knowledge, memory, tools, and human input to enable trustworthy, coherent, and contextual reasoning. As AI systems grow more dynamic and capable of long-running, complex interactions, the volume and diversity of context data—ranging from conversation history and memory states to real-time tool outputs and user intent—have become a critical challenge. Current approaches often fragment this information across isolated systems: databases, message queues, in-memory stores, and unstructured logs. This leads to difficulties in tracking, updating, debugging, and ensuring consistency across agents. The solution lies in unifying context under a single, coherent abstraction—one that treats all elements of an agent’s environment as files within a Virtual File System (VFS). Inspired by the foundational Unix principle that “everything is a file,” this architectural vision proposes a file-system abstraction as the backbone of AI Agent context management. It’s not merely a metaphor but a practical software framework that enables persistent, hierarchical organization of heterogeneous data types—memory stores, knowledge graphs, API responses, human feedback, tool outputs, and even transient states—into a unified, accessible, and governable structure. This system allows agents to mount, query, version, and control access to context with the same precision as managing files on a disk. It addresses the core question: “What information exists, and how is it organized?” But it doesn’t stop there. The real power emerges when humans step in as curators, verifiers, and co-reasoners, embedding tacit knowledge, intent, and judgment into the system—bridging the gap between machine logic and human understanding. The proposed framework includes three key components that form a closed-loop context engine: Context Constructor: Selects, prioritizes, and compresses relevant information from the repository, producing a traceable manifest of what’s being used. Context Updater: Streams and refreshes context incrementally into the model’s token window, ensuring up-to-date reasoning without overflow. Context Evaluator: Verifies outputs, detects hallucinations, and reintegrates validated information—often with human-in-the-loop review—ensuring reliability and accountability. This pipeline enables agents to reason coherently within constrained model contexts, maintain traceability, and adapt dynamically to changing conditions. It transforms ad-hoc, error-prone context handling into a reusable, scalable infrastructure. As generative AI moves into high-stakes domains like healthcare, legal support, and enterprise decision-making, the need for trustworthy, auditable, and human-aligned reasoning becomes non-negotiable. A file-system-based approach doesn’t just manage data—it creates a shared, persistent world model that agents can evolve over time, aligned with organizational goals and human values. Innovation in this space doesn’t require every idea to be implemented immediately. Research and prototyping are essential for exploring new perspectives and refining understanding. As the field matures, the most enduring solutions will likely be those that build on timeless principles—like the Unix file system—while solving today’s most pressing challenges. In the age of Agentic AI, where memory and context are central, prioritizing context engineering isn’t just a technical choice—it’s a strategic imperative. It defines how we scale intelligent systems responsibly, transparently, and in service of human needs.