PAL: Giving AI Agents Hands in the Physical World

REPL Is All You Need.

A 19-year-old's proposal for an open standard that lets AI Agent control hardware directly.

The Problem: AI Agents Are Trapped in Screens

AI agents can write code, search the web, deploy servers, and manage databases. They're incredibly capable — inside a container.

But ask your favorite AI to flip a relay, read a temperature sensor, or scan an I2C bus — and it can't. Not because it doesn't know how. It knows exactly what machine.Pin(5, Pin.OUT) does. It just has nowhere to run that code.

AI agents lack a physical execution terminal.

CLI tools gave LLMs hands in the digital world (df -h, pip install, docker compose up). Embedded systems can give them hands in the real world (GPIO.on(), ADC.read(), I2C.scan()). But nobody has defined a standard for how agents should talk to hardware.

That's what PAL is.

The Landscape: What Exists, and Why It Falls Short

ESP-Claw (Espressif Official)

Espressif's "Chat Coding" framework puts a full AI agent on an ESP32-S3 — ReAct loop, LLM calls, tool registry, IM channels (Telegram, WeChat), Event Router. It's impressive engineering. It's also the wrong architecture.

Agent runs on the MCU — 8MB PSRAM minimum, $15+ BOM
Uses Lua — LLMs generate Python 50x more accurately than Lua
Pre-registration required — every GPIO needs a C struct registered as a Lua tool module
LLM API calls from MCU — one network timeout can stall the entire FreeRTOS task

mimiclaw

A lighter ESP32 agent. Same architecture, GPIO bugs confirmed. Toy-grade.

Bare-metal C + UART commands

Rock solid. But adding a new operation means: write C → compile → flash → reboot. 10 minutes minimum. Agents can't iterate at that speed.

Raspberry Pi + GPIO

Python libraries everywhere, but 30-second boot, 5W power draw, $35+ cost, no hard real-time. Overkill for controlling a relay.

The Core Insight: MicroPython REPL IS the Agent Interface

Here's what everyone missed: Python's REPL and an AI agent's interaction model are isomorphic.

REPL loop:                    Agent loop:
  >>> type code                 receive task
  execute                       reason → generate code
  see output                    send code to REPL
  >>> type next                 observe result → adjust

You don't need a tool registry. You don't need JSON schema. You don't need a Skill Registry. The REPL is the world's simplest IPC.

And Python? It's the language LLMs generate best — 25% of GitHub public repos, versus <0.5% for Lua. Claude has seen millions of machine.Pin() calls in training data. It knows how to write this code.

The Architecture: PAL in Two Cores

┌──────────────────────────────────────┐
│         Cloud Agent (AstrBot)         │  ← Reasoning, planning
└──────────────┬───────────────────────┘
               │ WebSocket JSON
┌──────────────▼───────────────────────┐
│        ESP32-S3 PAL Terminal          │
│                                       │
│  Core 0 (C, FreeRTOS, NEVER CHANGES): │  ← Hard real-time
│  · SPI/I2C/UART drivers               │
│  · Hardware watchdog                  │
│  · WiFi auto-reconnect               │
│  · Pin ownership table                │
│                                       │
│  Core 1 (MicroPython, ANYTHING GOES): │  ← Agent playground
│  · WebSocket → JSON → Python exec     │
│  · machine module → hardware          │
│  · uasyncio → concurrent tasks        │
│  · Crash? Core 0 restarts you.        │
└──────────────────────────────────────┘

Core 0 is the brake pedal. It never changes. Core 1 is the steering wheel. The agent can grip it however it wants.

If the agent writes an infinite loop? Core 0 detects heartbeat timeout → restarts Core 1 VM. If the agent tries to access system pins? machine.Pin() returns OSError. If the agent crashes? Core 0 keeps running. Physical control link never breaks.

The Protocol: JSON That Executes Python

No tool schemas. No pre-registration. Just Python code over WebSocket:

Agent → Terminal:

{
  "version": "1",
  "id": "msg_001",
  "type": "exec",
  "code": "from machine import Pin; Pin(5, Pin.OUT).on()",
  "timeout_ms": 10000
}

Terminal → Agent:

{
  "version": "1",
  "id": "msg_001",
  "type": "result",
  "stdout": "",
  "stderr": "",
  "error": false,
  "exec_time_ms": 12
}

That's it. One round-trip over WiFi: <10ms. Python execution: <1ms.

Why Cloud Agent, Not On-Device Agent

1. Brain and hands should use different hardware

Physical execution needs determinism, low latency, 24/7 stability. AI reasoning needs elastic compute, large memory, frequent iteration. Forcing both onto one MCU is asking one chip to do two contradictory things.

2. One brain, many hands

A single cloud agent can manage dozens of PAL terminals across a factory floor. Agent-on-Device requires one agent instance per node — no global perspective.

3. You can stop the brain anytime

Cloud agent misbehaving? Cut the WebSocket. It's over. Agent-on-Device misbehaving on an MCU? You wait for the hardware watchdog to trigger — that's your only recovery mechanism.

4. Skills, MCP, tools run on cloud infrastructure

Hermes-style skill accumulation, vector databases, MCP tool chains, SQLite persistence — all mature AI infrastructure. None of it needs to be squeezed into 8MB of PSRAM.

What PAL Is, and What It Isn't

PAL IS:

✅ An agent-friendly embedded terminal standard (ESP32-class, MicroPython, dual-core)
✅ A JSON protocol for executing Python code on hardware
✅ A safety model (5-layer defense, pin ownership, Core 0 isolation)

PAL IS NOT:

❌ An I2C/SPI bus protocol
❌ An Agent framework
❌ A hardware reference design
❌ Tied to any specific MCU or peripheral

Current Status

PAL is a draft specification (v0.1). I'm a freshman at Anhui University of Science and Technology. The reference implementation (ESP32-S3 Core 0/1) is under development.

Note: I'm currently preparing for exams and still learning how to express technical ideas fluently in English, so I used Claude to help draft and polish this post. All ideas, architecture, and the PAL specification itself are my own work.

This spec is open for discussion. I'm looking for: