191 lines
6.3 KiB
Markdown
191 lines
6.3 KiB
Markdown
---
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title: AI tool to try and implement
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type: area
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status: active
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created: "2026-11-05 20:38"
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updated: "2026-09-26"
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tags:
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- ai
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- tool
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- dev
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- framework
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- implement
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modified: "2026-11-05 20:38"
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---
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# [[AI tool to try and implement - MOUSE-1]]
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# Integrating Mouse-1 and Switch into an AI Engineering Stack
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This document details how to integrate **Mouse-1** (a low-latency, System 1 decision-routing model) alongside the **Switch** framework inside your **Pi Dev (`pi.dev`)** environment.
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---
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## 1. Architectural Overview
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In a production AI agent or DevOps pipeline, offloading classification, routing, and safety filtering to a frontier reasoning model (like Claude or GPT) is slow and expensive.
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Instead, use a tiered system:
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* **System 2 (Slow/Complex):** Large frontier models handle planning, code generation, and complex reasoning.
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* **System 1 (Fast/Deterministic):** **Mouse-1** handles 100ms routing, tool gating, and error handling via fixed token probabilities.
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[Agent State / Log Output]
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│
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▼
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┌───────────────────────┐
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│ osolmaz/switch │ <-- Evaluates variables, structures payload
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└───────────┬───────────┘
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│
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▼
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┌───────────────────────┐
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│ Mouse-1 │ <-- Evaluates probability of choices in ~100ms
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└───────────┬───────────┘
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│
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[Probabilities]
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│
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▼
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┌───────────────────────┐
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│ Pi Dev Extension │ <-- Blocks tool, executes rollback, or continues
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└───────────────────────┘
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---
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## 2. Core Stack Components
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### Mouse-1 (Decision Model)
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* **API Endpoint:** `https://mouse.dev`
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* **Pricing:** Flat \$0.10 per 1 million input tokens (outputs/probabilities are free).
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* **Primary Benefit:** Supports native LoRA fine-tuning via `/v1/calibrations` to realign routing decisions based on your unique system logs.
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### Switch (Dynamic Router)
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* **Repository:** [osolmaz/switch](https://github.com)
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* **Primary Benefit:** A declarative routing package that maps systemic states to execution branches. It prevents messy `if/else` logic when preparing schema choices for a decision layer.
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---
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## 3. Implementation in Pi Dev (`pi.dev`)
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Pi Dev uses a TypeScript extension system to intercept and gate operations. The best way to implement Mouse-1 is at the **tool lifecycle layer**, intercepting tool calls or terminal inputs before execution.
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### File: `extensions/mouse-guard.ts`
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```typescript
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import { Extension, ToolCallEvent } from '@earendil-works/pi-agent-core';
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export const MouseGuardExtension: Extension = {
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name: 'mouse-guard',
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init(context) {
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// Intercept tool execution events before they hit the operating system
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context.events.on('tool:beforeCall', async (event: ToolCallEvent) => {
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// Bypass evaluation for safe read operations
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if (event.toolName === 'read_file') return;
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// Extract system context for the state string
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const state = `The agent is attempting to run tool: "${event.toolName}" with args: ${JSON.stringify(event.args)}. CWD State: ${context.projectSummary}`;
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// Define explicit, deterministic options
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const options = [
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"allow_execution",
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"block_and_request_human_review",
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"abort_with_error"
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];
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try {
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const response = await fetch('https://mouse.dev', {
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method: 'POST',
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headers: {
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'Content-Type': 'application/json',
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'Authorization': `Bearer ${process.env.MOUSE_API_KEY}`
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},
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body: JSON.stringify({
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model: 'mouse-1',
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state: state,
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options: options
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})
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});
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const data = await response.json();
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const probabilities = data.probabilities;
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// Human-in-the-loop gate: Block if threshold for human review exceeds 70%
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if (probabilities.block_and_request_human_review > 0.70) {
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event.cancel("Execution blocked by Mouse-1: High probability of safety or structural risk.");
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}
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} catch (error) {
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// Fail-safe open if the API fails
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console.error("Mouse-1 decision layer unreachable. Defaulting to safe-open.", error);
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}
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});
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}
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};
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```
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---
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## 4. Using `switch` to Structure Python Pipeline Decisions
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When orchestrating background DevOps flows (e.g., automated error recovery, CI/CD self-healing), use `switch` to map your application states directly to dynamic payloads.
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### File: `devops_router.py`
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```python
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import requests
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import os
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from switch import Router
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router = Router()
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# Define explicit deployment branches
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@router.case("run_helm_rollback")
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def handle_rollback(log_context):
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print("Executing automatic helm rollback...")
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# os.system("helm rollback ...")
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@router.case("trigger_slack_alert")
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def handle_escalation(log_context):
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print("Escalating critical error to engineering Slack channel...")
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# Orchestrate the Mouse-1 lookup layer
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def evaluate_and_route(error_logs: str):
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# Dynamically extract potential options from our switch cases
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options = list(router.cases.keys())
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payload = {
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"model": "mouse-1",
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"state": f"CI/CD Pipeline Failure. Error Logs:\n{error_logs}",
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"options": options
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}
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headers = {
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"Authorization": f"Bearer {os.getenv('MOUSE_API_KEY')}",
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"Content-Type": "application/json"
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}
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# Query decision layer
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response = requests.post("https://mouse.dev", json=payload, headers=headers).json()
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probabilities = response["probabilities"]
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# Select the branch with the highest confidence
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best_choice = max(probabilities, key=probabilities.get)
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confidence = probabilities[best_choice]
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print(f"Mouse-1 selected branch: {best_choice} ({confidence*100:.1f}% confidence)")
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# Route execution via switch framework if confidence hits the SLA threshold
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if confidence > 0.85:
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router.route(best_choice, error_logs)
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else:
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router.route("trigger_slack_alert", error_logs)
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if __name__ == "__main__":
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sample_crash = "Fatal Error: OOM Killed on pod replica-abc-123. Memory limit exceeded."
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evaluate_and_route(sample_crash)
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```
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```` |