Task-Aware Harness Provisioning for LLM Agents in Mission-Critical Infrastructure Operations

The paper introduces a task-aware harness provisioning method for large language model agents used in mission‑critical infrastructure operations. By mapping each task to an optimal harness configuration—determined through mathematical task classification and empirical agent execution data—the authors propose a map‑guided escalation algorithm that starts with a minimal harness and expands only when necessary. Experiments on liquid‑cooling and power‑grid tasks show that this approach can improve accuracy and reduce token usage compared to full harness provisioning, revealing a domain‑dependent accuracy‑cost trade‑off.

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• llms
• agents