FPF Agent Stack
Bounded Context: Offline AI Agent Runtime
1. Context
Problem. Cloud LLM APIs leak private data, incur per-token costs, and require network connectivity.
Solution. An embedded, offline agent runtime with auditable execution and tri-state safety guards.
Scope. Local development, private automation, air-gapped environments.
2. Intent (L-PROJ-*)
L-PROJ-01: Core Capabilities
Capability | Description |
Embedded Model | Qwen 2.5 0.5B Instruct via Transformers.js — no API keys, no network |
MCP Server | Exposes skills as tools via Model Context Protocol (Stdio) |
AgentFS | Copy-on-write sandbox for safe, auditable file operations |
Guard | Tri-state validation: pass / degrade / abstain |
Skills | Dynamic loading from |
L-PROJ-02: Project Structure (FPF E.4 Compliant)
3. Archetypal Grounding
U.System Example: AgentFS Session
U.Episteme Example: Skill Contract
4. Getting Started
Prerequisites
Node.js 20+ (LTS)
8GB RAM minimum
Setup
Run
5. Conformance Checklist
ID | Requirement | Verification |
CC-PROJ-01 | Runs without network |
|
CC-PROJ-02 | Model loads <30s | Manual timing on 8GB machine |
CC-PROJ-03 | File writes sandboxed |
|
CC-PROJ-04 | Unknown tools abstain |
|
6. Relations
Builds on. FPF L/A/D/E pattern, MCP specification, Transformers.js.
Constrains. All skill implementations must follow SKILL.md template.
Coordinates with. Technical-Choices.md, Development-Plan.md, Architecture-Comparison.md.