Context Constructor, Loader, and Evaluator

• Context Constructor, Loader, and Evaluator are key modules in a file-system-oriented context engineering pipeline for GenAI, ensuring rigorous context artefact management.
• The Constructor selects, prioritizes, and compresses artefacts using relevance metrics and token constraints, generating an auditable manifest for LLM prompts.
• The Loader and Evaluator integrate context injection with confidence scoring and human oversight, enabling accountable and traceable GenAI responses.

Context Constructor, Loader, and Evaluator are the principal components of a verifiable, file-system-oriented context engineering pipeline designed for agentic generative AI (GenAI) systems. These modules, formalized and implemented within the AIGNE framework, enable rigorous assembly, delivery, and validation of context artefacts—encompassing history, memory, tools, and human input—under explicit token and governance constraints. The architecture employs a unified file system abstraction ("everything is a file") to persist, mount, trace, and audit all context manipulation, superseding fragmented prior practices such as prompt engineering and retrieval-augmented generation (Xu et al., 5 Dec 2025).

1. Formal Role Definitions and Responsibilities

Context Constructor

The Context Constructor is a software module responsible for selecting, prioritizing, and compressing context artefacts responsive to a task request or user prompt. Artefacts are discovered by querying the mounted file-system namespace ("/context"), are scored by relevance metrics (e.g., semantic similarity, recency), and are subject to strict access control and data-governance enforcement. The combined context is trimmed or summarized to satisfy the aggregate token constraint:

$\sum_i \text{tokens}(c_i) \leq T_\text{max}$

The Constructor emits a manifest (JSON structure), detailing which files are included, their selection order, and justifications anchored in computed relevance.

Context Loader (Updater)

The Loader ingests the manifest and composes the LLM prompt buffer by physically reading and injecting the specified context fragments. The Loader supports both static (single-shot) and streaming (incremental) modes, crucial for multi-turn dialogs or large context queues. Token usage is continuously monitored to enforce $T_\text{max}$; fragment replacement or appending adapts to evolving conversational state. Every context injection or update is logged with full metadata (e.g., session ID, timestamp, source path).

Context Evaluator

The Evaluator, post inference, validates LLM outputs against the factual source context by extracting and comparing atomic statements with reference artefacts. A formal confidence score is computed:

$$\text{confidence} = \frac{|\text{extractedFacts} \cap \text{referenceFacts}|}{|\text{extractedFacts}|}$$

If confidence falls below a configured threshold ($\theta$), human-in-the-loop verification is triggered. Otherwise, validated knowledge is re-integrated into long-term stores (e.g., "/context/memory/…"), annotated with full lineage (createdAt, sourceManifest, revisionId) and audited through appending to the transaction log.

2. File System Abstraction (“Everything is a File”)

All context artefacts—tools, agent memory, human annotations—reside under a unified namespace managed by an Agentic File System (AFS). Artefacts are addressed by canonical paths, e.g., "/context/history/", "/context/memory/{agentID}/", "/context/pad/{taskID}/", "/context/tools/", and "/context/human/". AFS supports external "mounting" of data sources (vector DBs, knowledge graphs, MCP protocol servers), exposing programmable resolvers at arbitrary mount-points.

Key file system operations include:

• afs.list(): List files in a namespace.
• afs.read(): Read artefact content.
• afs.search(): Search files by content or metadata.
• afs.write(): Persist updates or derived artefacts.
• afs.exec(): Invoke mounted tool interfaces.

Each artefact is accompanied by structured metadata (createdAt, owner, permissions, sourceId, version, etc.). Access control policies are enforced per-path, and every operation generates a log entry ("/context/logs/") to guarantee traceability and future auditability.

3. Pipeline Architecture and Workflow

The pipeline's architecture is modular and staged, beginning with an external task or user prompt and ending with persisted, validated memory. The flow is orchestrated as follows:

1. Agent Request / User Prompt initiates the process.
2. Context Constructor mounts the AFS, queries and ranks artefacts, enforces governance, and outputs a manifest (JSON) of selected files and rationale.
3. Context Loader reads listed artefacts and assembles the LLM prompt. Static and streaming update modes are supported.
4. LLM API produces the completion, considering the injected context.
5. Context Evaluator compares model statements to sourced artefacts, computes consistency, and triggers human review if required.
6. Audit + Memory File System receives updates and full lineage, supporting review, rollback, and compliance.

Key AFS namespaces:

Path	Role
/context/history/	Immutable logs
/context/memory/{agentID}/	Long-term facts and summaries
/context/pad/{taskID}/	Ephemeral scratchpad
/context/tools/	Tool definitions, MCP mounts
/context/human/	Human reviews and annotations

4. Algorithms and Pseudocode

The framework provides precise module algorithms, paraphrased below for brevity:

Context Constructor

• List candidates in "/context"
• For each accessible file:
  • Compute embedding, recency, composite score: $$score = \alpha \cdot \text{cosine}(\text{embed(query)}, \text{embed(file)}) - \beta \cdot \text{recency}$$
  • Accumulate and sort candidates by score
  • Add or summarize candidates to fit under token budget $T_\text{max}$
• Emit manifest recording selected files, order, and justifications

Context Loader

• For each entry in manifest:
  • Read and concatenate fragments, or yield incrementally if streaming
  • Record each load/replace operation with metadata

Context Evaluator

• Extract facts from LLM response
• Load reference facts from manifest
• Compute intersection-based consistency score
• Trigger human review if below threshold, else persist new knowledge into memory with metadata

Pseudocode structures for each step and the mathematical formula for consistency scoring are explicitly provided in (Xu et al., 5 Dec 2025).

5. Real-World Applications

The pipeline architecture underpins several agentic GenAI exemplars:

• Memory-enabled chatbot: The Constructor selects recent dialogue from "/context/history", Loader replays them, Evaluator persists new user preferences into "/context/memory/user/…".
• GitHub MCP assistant: MCPAgent mounted at "/modules/github-mcp" enables contextual data retrieval by the Constructor, Loader streams the output, Evaluator persists issue triage outcomes or summaries into memory.

Both applications illustrate strong coupling between pipeline modules and the "everything is a file" abstraction, with rigorous selection, delivery, and evaluation processes for context artefacts.

6. Traceability, Accountability, and Governance

Every pipeline action—context construction, loading, evaluation—produces a log entry enriched with session identity, timestamps, component, touched files, and key decisions. Manifests provide auditable evidence for input selection and ordering. Versioned writes in "/context/memory/…" carry sourceManifest and revisionId for full lineage.

Human overrides are isolated under "/context/human/…", original model outputs preserved. Access controls ensure multi-agent and multi-tenant isolation. The logging directory "/context/logs/" enables post-hoc replay, compliance audit, and rollback, constituting a foundation for transparent, accountable AI co-work.

7. Significance in Context Engineering

This architecture renders context engineering into a disciplined, auditable, modular process, establishing a persistent substrate for all context artefacts. The pipeline is extensible: new data sources can be mounted, custom module hooks developed, and full governance exercised over the context lifecycle. This enables maintainable, verifiable, and industry-ready GenAI systems that elevate human curation, verification, and joint reasoning as first-class obligations (Xu et al., 5 Dec 2025).