Edge Fractal Agent

---
name: Edge Fractal Agent
description: Edge-Native Fractal Agent Engine Blueprint for sub-second, zero-cost, offline autonomous agent execution.
---

# 🚀 Edge-Native Fractal Agent Engine: Strategic Blueprint

> **Goal**: Pair a hyper-specialized Edge Small Language Model (SLM) with the `fractal-kernel` Node.js manifest architecture to achieve sub-second, zero-cost, offline autonomous agent execution on standard edge CPUs (Intel i3 / Raspberry Pi / Edge Nodes).

---

## 🏗️ The 3-Pillar Architecture

```
┌─────────────────────────────────────────────────────────────────────────┐
│                          EDGE SLM BRAIN (1.5B)                          │
│  - Fine-Tuned Qwen2.5-1.5B / Phi-4-mini (Quantized Q4_K_M: ~1.1GB RAM)  │
│  - Speed: 50-80 tokens/sec on Intel i3 CPU                              │
└────────────────────────────────────┬────────────────────────────────────┘
                                     │
                                     ▼
┌─────────────────────────────────────────────────────────────────────────┐
│                    VIRTUAL SIMULATION GUARDRAIL                        │
│  - Runs candidate manifests & routing in-memory before mounting         │
│  - Zero file system writes until dry-run verification succeeds          │
└────────────────────────────────────┬────────────────────────────────────┘
                                     │
                                     ▼
┌─────────────────────────────────────────────────────────────────────────┐
│                     FRACTAL-KERNEL RUNTIME ENGINE                       │
│  - Auto-discovers self-contained feature modules                        │
│  - Mounts dynamic routes and executes Node.js business logic            │
└─────────────────────────────────────────────────────────────────────────┘
```

---

## 🎯 1. Model Selection & Fine-Tuning Strategy

### Target Base Model
* **Primary Recommendation**: `Qwen/Qwen2.5-1.5B-Instruct` or `Qwen2.5-Coder-1.5B`
* **RAM Footprint**: **~1.1 GB RAM** (4-bit `Q4_K_M` GGUF)
* **Execution Latency**: **< 200 ms initial response, 60+ tokens/sec on i3 CPU**

### Training Focus (The Secret to Extreme Quality at 1.5B Parameters)
Rather than general knowledge, the 1.5B model is fine-tuned **exclusively** on 3 task vectors:
1. **Manifest Generation**: Strict, deterministic creation of `fractal-kernel` JSON feature manifests.
2. **Dynamic Routing**: Evaluating incoming request payloads and mapping them to module routes.
3. **Simulation Tracing**: Generating internal dry-run validation checks to detect invalid schemas.

---

## 🧪 2. Dataset Synthesis Pipeline for `fractal-kernel`

To make a 1.5B model out-perform a generic 70B model on your specific architecture, we build a **High-Density Synthetic Dataset** containing 2,000 instruction pairs:

```json
{
  "messages": [
    {
      "role": "system",
      "content": "You are the Fractal-Kernel Edge Routing SLM. Generate precise feature manifests and dry-run simulation checks."
    },
    {
      "role": "user",
      "content": "Add an automated dynamic rate-limiter feature module."
    },
    {
      "role": "assistant",
      "content": "```json\n{\n  \"simulation\": {\n    \"dry_run\": \"PASSED\",\n    \"memory_impact_mb\": 4.2,\n    \"dependencies\": [\"redis-cache\"]\n  },\n  \"manifest\": {\n    \"name\": \"rate-limiter\",\n    \"version\": \"1.0.0\",\n    \"mountPath\": \"/api/middleware/rate-limit\",\n    \"features\": [\"sliding-window\", \"ip-throttle\"]\n  }\n}\n```"
    }
  ]
}
```

---

## 🔌 3. Native Node.js `fractal-kernel` Integration

Deploy the fine-tuned model directly inside your Node.js Kernel using `node-llama-cpp` for zero-overhead native C++ bindings:

```javascript
// edge-agent-kernel.js
import { LlamaModel, LlamaContext, LlamaChatSession } from "node-llama-cpp";
import path from "path";

class FractalEdgeAgent {
  async init() {
    this.model = new LlamaModel({
      modelPath: path.join(__dirname, "models", "fractal-agent-1.5b-q4.gguf")
    });
    this.context = new LlamaContext({ model: this.model });
    this.session = new LlamaChatSession({ contextSequence: this.context.getSequence() });
  }

  async processRequest(userPrompt) {
    // Execute sub-second local inference
    const response = await this.session.prompt(userPrompt);
    const parsed = JSON.parse(response);

    // Enforce Virtual Simulation Check before mounting
    if (parsed.simulation?.dry_run === "PASSED") {
      this.mountFeatureModule(parsed.manifest);
    } else {
      console.warn("Simulation failed. Aborting feature mount.");
    }
  }
}
```

---

## 🏁 4. Execution Roadmap

1. **Dataset Build**: Generate 2,000 JSON instruction pairs tailored to `fractal-kernel`.
2. **Kaggle Training**: Train `Qwen2.5-1.5B` via LoRA 4-bit on Kaggle dual T4 GPUs (~45 minutes execution).
3. **GGUF Export**: Export to `Q4_K_M` GGUF (~1.1 GB file).
4. **Edge Benchmark**: Mount in Node.js via `node-llama-cpp` on your i3 PC and measure sub-second response times.