This study proposes a conceptual framework for harness engineering for artificial intelligence (AI) agents based on large language models (LLMs), with the aim of clarifying the role of the infrastructure layer between model reasoning capabilities and the operational requirements of real-world information technology (IT) environments. The study employs an exploratory sequential mixed-methods design comprising three stages: a systematic scoping review of 147 publications published between 2021 and 2026, benchmark experiments involving three harness configurations across five evaluation suites, and semi-structured interviews with 12 IT infrastructure experts from universities in Vietnam and several Southeast Asian countries. The synthesis identifies six functional pillars of harness engineering: instruction rules, automated sensors, agentic loops, external memory, access control, and system observability. Across all five benchmarks, the full harness configuration demonstrated higher reported accuracy and better post-failure recovery than the default configuration in the descriptive comparisons. Interview analysis indicates that the most prominent barriers to implementation in universities are a lack of technical expertise (10/12, 83.3%), incompatibility with legacy infrastructure (9/12, 75.0%), and cybersecurity concerns (8/12, 66.7%). The findings suggest that harness engineering should be treated as an engineering layer distinct from model optimization and implemented through a phased roadmap supported by internal capacity building, reference architectures, and appropriate governance and monitoring mechanisms.