AIGNE Framework: Governed Context in GenAI

• AIGNE is a unified file-system framework that virtualizes heterogeneous context artifacts—external knowledge, memory, human inputs—as standardized files for GenAI.
• It employs transactional logging and audit trails to ensure traceability and accountability across the context-engineering pipeline.
• Its modular design enables seamless integration of new sources and tools, reducing context fragmentation and enhancing decision support.

The AIGNE (Agentic File System for Context Engineering) framework constitutes a governed, file-system-based infrastructure for managing heterogeneous context artifacts in Generative AI (GenAI) systems. Drawing from the Unix principle that “everything is a file,” AIGNE virtualizes all resources—external knowledge, memory, tool interfaces, and human annotations—within a unified, persistent hierarchy underpinned by rigorous metadata and access controls. The framework operationalizes a context-engineering pipeline composed of distinct, auditable stages, enabling verifiable, maintainable, and industry-ready GenAI deployments in human-centered decision-support settings (Xu et al., 5 Dec 2025).

1. Design Rationale: From Ephemeral Context to Governed Substrate

Context engineering addresses core deficiencies of contemporary GenAI deployment techniques. Traditional prompt engineering, retrieval-augmented generation (RAG), and tool integrations yield fragmented and ephemeral context, obstructing traceability, accountability, and long-term auditability. AIGNE formalizes a substrate where all context artifacts—from memory stores and external knowledge bases to annotated human input—appear as files or directories. This abstraction delivers:

• Abstraction and Encapsulation: Back-end heterogeneity (databases, APIs, human filesystems) is hidden behind file operations (list, read, write, execute).
• Modularity and Mountability: New sources or tools are modularly mounted as independent subtrees, each governed by resolvers.
• Separation of Concerns: Data, tool access, and governance each reside in dedicated directory hierarchies.
• Traceability: All file operations, context construction steps, and reasoning events are continuously logged as file-system transactions.
• Composability and Evolvability: Integration of new tools, models, or human contributions requires only new mounts/resolvers; core agent code remains unchanged.

The persistent file-system metaphor ends the reliance on throwaway scripts or prompts, transforming context into a durable, accountable software component.

2. Core Architecture: The Agentic File System (AFS)

AIGNE’s architecture is instantiated atop the Agentic File System (AFS), which undergirds three coordinated components—Context Constructor, Context Loader (Updater), and Context Evaluator.

Context Repository Structure:

• /context/history/: Immutable event logs containing every raw input, model response, and intermediate scratchpad, each timestamped and version-tracked.
• /context/memory/{agentID}/[episodic|fact|procedural|profile]/: Structured memory entries as text, JSON, embeddings, or triples.
• /context/pad/{taskID}/: Ephemeral working directories for scratchpads, pruned after completion.

Context Constructor: Accepts queries or system instructions and enumerates context files using afs_list() and afs_read(), ranking fragments by recency, provenance, and metadata. Relevant fragments are compressed and assembled to respect token constraints, captured in a JSON context manifest.

Context Loader (Updater): Ingests the manifest, model state, and active dialogue. It injects context using static (single-turn), incremental (streaming), or adaptive (feedback-driven) modes. It guarantees $\sum_{i=1}^n \ell(c_i) \leq T$ (with $\ell$ as token length and $T$ as the token window), assembling an ordered prompt buffer for the LLM.

Context Evaluator: Receives outputs, verifies factual consistency against original sources, scores confidence, detects hallucinations, and annotates memory with verified information (or triggers human review for low-confidence/contradictory outputs). Audit trails capture verification, rollbacks, and human overrides.

3. Uniform Resource Representation and Mounting

All resources are represented and accessed via standardized file paths, with resolvers providing virtualization and integration across back-ends.

Resource Type	File-System Path Example	Metadata Fields (Sample)
External Knowledge	/context/knowledge/	owner, tokenLength, provenance, createdAt
Memory Entries	/context/memory/{agentID}/{type}/	owner, sourceId, confidence, modifiedAt
Tools & Functions	/modules/	input_schema, output_schema, ACL
Human Inputs	/context/human/	reviewer ID, version, correction reason

Respective resources are mounted using resolvers (e.g., afs.mount(new VectorDBResolver(...)) for vector databases; afs.mount(mcpAgent) for external modular command processors). Sandbox isolation restricts agent visibility to authorized subtrees.

Access control is formalized as: $$\mathrm{AccessControl}(u,f) = \begin{cases} 1 & \text{if } u \in \mathrm{ACL}(f) \ 0 & \text{otherwise} \end{cases}$$

4. Verifiable Context-Engineering Pipeline

AIGNE’s provenance model ensures all context construction, delivery, validation, and memory updates are not only logged but also replayable for audit. Major features:

• Token-Budgeted Assembly: Constructors emit manifests documenting rationale for inclusion/exclusion of each fragment.
• Transactional Logging: All compression, read, replace, and validation events are written as file-system transactions with full provenance.
• Persistent Governance: Memory updates are lineage-tracked to their source and session. Scratchpads expire post-task, while history is compressible append-only.
• Auditability: Sessions are replayed deterministically from file-system event logs (<100 ms overhead in benchmarks).

5. Human-Centric Roles in the Loop

Human involvement is a core design principle in AIGNE, distinguishing among three primary roles:

• Curators: Define, configure, and mount new context sources.
• Verifiers: Review outputs flagged as low confidence (confidence $<\tau$); corrections are appended to /context/human/.
• Co-reasoners: Directly interact with intermediate scratchpads, contributing hypotheses and direct memory writes to elevate tacit organizational knowledge.

The operational workflow is: User → Prompt → Constructor → Loader → LLM → Evaluator → [Model OK] → Memory / [Low-confidence] → Human Review → Memory.

6. Representative Applications and Empirical Evaluation

Agent with Memory:

A TypeScript setup mounts persistent history and user profile memory; every dialogue turn is appended to /context/history/, summarized into session memory, and auto-loaded across sessions.

• Empirical findings: over 20-turn dialogues, agents exhibited less than 10% decrease in context-relevance compared to stateless baselines.

MCP-based GitHub Assistant:

A modular GitHub agent is mounted as /modules/github-mcp/ (e.g., via Docker), enabling direct file-system calls and command executions.

• Lab user study: developer task completion reduced by ~30% compared to baseline.

7. Implementation Stack and Industry Readiness

• Language and Core: TypeScript/Node.js; core modules include AFS abstraction, SystemFS resolver, FSMemory, and UserProfileMemory.
• Integration: Native adapters for OpenAI, Gemini, Claude, DeepSeek, Ollama; plugin-support for vector DBs and knowledge graphs; full-text search via ripgrep.
• Codebase Organization:
  • /core: agent runtime
  • /afs: file system abstraction
  • /memory: history, scratchpad, episodic modules
  • /modules: modular tooling (e.g., MCPs)
• Quantitative Outcomes:
  • Verifiability: 100% of context-assembly steps auditable, deterministic replay in <100 ms.
  • Maintainability: new memory types integrated in under 2 hours.
  • Industry deployments: two pilot projects implemented with zero changes to core logic—solely new mounts.

AIGNE’s abstraction definitively formalizes context as a first-class, governed, and persistent asset for GenAI systems, addressing the fragmentation and rot endemic in prior ad-hoc, prompt-driven pipelines (Xu et al., 5 Dec 2025).