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Task-Aware Harness Provisioning Optimizes LLM Agents for Critical Infrastructure.

Liangtao Lin, Qingang Zhang, Zhaomeng Zhu, Tianwei Zhang, Yonggang Wen· August 19, 2026 View original

Key takeaways

  • LLM agents in critical infrastructure can benefit from task-aware harness provisioning.
  • Optimal harness configurations vary by task, avoiding a one-size-fits-all approach.
  • Map-guided escalation reduces token usage and can improve agent accuracy.
  • There's a domain-dependent accuracy-cost trade-off, not a universal optimum.

Who benefits

UtilitiesEnergyManufacturingTransportationAI Development

Summary

This paper proposes a task-aware harness provisioning algorithm for large language model agents operating mission-critical infrastructure, optimizing resource allocation by matching harness configurations to specific task requirements. This method improves accuracy and reduces token usage compared to full provisioning, demonstrating a domain-dependent accuracy-cost trade-off.

Large language model (LLM) agents are increasingly deployed in mission-critical infrastructure (MCI) operations, where they rely on a "harness" to define their access to information, tools, and actions. Current practices often provide a comprehensive, one-size-fits-all harness for every task, which can lead to inefficient resource use and potentially suboptimal performance. Researchers address this by framing harness provisioning as a resource-matching problem, aiming to identify optimal harness configurations for specific MCI tasks. They classify tasks based on the underlying system's mathematical representation and rank harness configurations by the information they provide. Task-to-harness mappings are derived from literature and controlled agent execution. The proposed "map-guided escalation" algorithm starts with a minimal, task-specific harness and only expands to full provision if a self-check fails. Evaluations in liquid cooling and power grid tasks show that this method can improve agent accuracy (e.g., from 0.652 to 0.715 in liquid cooling) and significantly reduce token usage (48% fewer tokens) while achieving comparable accuracy to more complex methods like Reflexion. The findings highlight a domain-dependent accuracy-cost Pareto frontier, suggesting no universal optimum.

Why it matters

For professionals managing critical infrastructure or developing AI agents for such environments, this research offers a pathway to more efficient, cost-effective, and potentially more accurate agent deployments. Optimizing harness provisioning can reduce operational costs and improve the reliability of AI-driven systems.

How to implement this in your domain

  1. 1Analyze existing LLM agent tasks in critical infrastructure for their specific information and tool requirements.
  2. 2Develop a classification system for tasks and harness configurations based on their operational needs.
  3. 3Implement a "map-guided escalation" strategy for harness provisioning, starting with minimal access and expanding only as needed.
  4. 4Benchmark the accuracy and token usage of task-aware provisioning against full provisioning in relevant MCI scenarios.

Original post by Liangtao Lin, Qingang Zhang, Zhaomeng Zhu, Tianwei Zhang, Yonggang Wen

"arXiv:2608.17433v1 Announce Type: new Abstract: LLM agents have been widely adopted to operate mission-critical infrastructure (MCI). These agents normally rely on a harness that determines what information they can access, which tools they can use, and what actions they can take…"

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Originally posted by Liangtao Lin, Qingang Zhang, Zhaomeng Zhu, Tianwei Zhang, Yonggang Wen on X · view source

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