LLM Agentic Playground

This page introduces the LLM Agentic Playground, a modular environment to test and evaluate multi-agent setups for control of liquid cooling systems.

Agentic LLM System Overview

Configurable Reasoning for Trade-Off Testing

Users can configure different reasoning parameters to explore the trade-offs between decision quality, latency, and memory usage.

  • LLM Models: Llama 8B, Qwen 8B

  • Context Window Sizes: 8K to 128K

  • Reasoning Modes: Chain of Thought, Few-Shot, Extended Thinking

Drag and Drop Agent Design for Scalable Control

A visual agent builder enables users to define scalable multi-agent architectures. These can be used for both coarse- and fine-grained control strategies in digital twins for liquid cooling.

Agentic LLM System Overview

Stress Injection for Reasoning Evaluation

System stress can be simulated through fault injection, allowing users to evaluate how various agent setups and reasoning styles impact:

  • System resilience

  • Fault recovery time

  • Control robustness

Real-Time Monitoring

Dashboards provide visibility into:

  • Agent and message flow

  • Event alerts

  • Key performance indicators (KPIs) related to liquid cooling

  • Control latency and dynamics

Explainability Tools

The system includes natural language explanation tools that provide:

  • Short-form rationales

  • Detailed reasoning traces

These help users understand decision-making processes and compare performance across configurations.

Safety and Guardrails

Built-in safety constraints ensure that all tests remain within operational boundaries. This allows users to:

  • Explore aggressive optimization strategies

  • Avoid causing system damage