# 2. Core Technology Architecture

#### 2.1 MCP-Based Scheduling Engine

ClawFuel implements the **MCP**. This allows any AI Agent (Claude, OpenClaw, Molt) to see GPU resources as a standardized "Context."

* **Native Discovery:** Agents can "see" available VRAM, TFLOPS, and Node locations.
* **Automated Execution:** Agents can push Python scripts, model training, or rendering tasks directly to the grid.

#### 2.2 Clawd Technology Integration

Using the architecture popularized by **Peter Steinberger’s OpenClaw**, ClawFuel acts as a Skill for the agent.

* **Agentic Shell Access:** The agent uses a secure shell to manage the remote GPU.
* **Autonomous Settlement:** The agent uses its own $CLAWFUEL wallet to pay for the seconds of compute it consumes.

#### 2.3 AI-Enhanced GPU Automation

We retain the core optimization power of the original project:

* **Auto-Tune Hashrates:** Real-time optimization of nodes.
* **Predictive Load Balancing:** AI-driven distribution of agent tasks to the most efficient nodes.


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